VIBRATION DIAGNOSIS OF BLADES OF ROTATING
MACHINES
A thesis submitted to the University of Manchester for the degree of
Doctor of Philosophy
in the Faculty of Engineering and Physical Sciences
2015
AHMED GUBRAN
SCHOOL OF MECHANICAL, AEROSPACE AND CIVIL
ENGINEERING
TABLE OF CONTENTS
2
TABLE OF CONTENTS
TABLE OF CONTENTS ........................................................................................................... 2 List of Figures ............................................................................................................................... 5
List of Tables .............................................................................................................................. 11
List of Nomenclatures ................................................................................................................. 13
List of Abbreviations .................................................................................................................. 15
ABSTRACT ............................................................................................................................... 17
DECLARATION....................................................................................................................... 18
COPYRIGHT STATEMENT .................................................................................................. 19
ACKNOWLEDGEMENTS ..................................................................................................... 20
DEDICATION........................................................................................................................... 21
CHAPTER 1 INTRODUCTION ...................................................................................... 22
1.1 General Introduction ....................................................................................................... 22
1.2 Motivation ......................................................................................................................... 28
1.3 Objective ........................................................................................................................... 29
1.4 Thesis outline .................................................................................................................... 29
1.5 Research contribution ...................................................................................................... 31
1.6 Publications ....................................................................................................................... 31 1.6.1 Journal publications ................................................................................................... 31
1.6.2 Conferences publications ........................................................................................... 31
CHAPTER 2 LITERATURE REVIEW .......................................................................... 33
2.1 Introduction ...................................................................................................................... 33
2.2 Condition monitoring in rotating machines ................................................................... 33
2.3 Vibration-based fault diagnosis ...................................................................................... 34
2.4 Blades Vibration ............................................................................................................... 35
2.5 Blade failure modes .......................................................................................................... 37 2.5.1 Blades with mistuned effects ..................................................................................... 37
2.5.2 Blade root looseness .................................................................................................. 38
2.5.3 Cracks on blades ........................................................................................................ 39
2.5.4 Blade flutter ............................................................................................................... 40
2.5.5 Blade rubbing ............................................................................................................ 40
2.5.6 Blade fatigue .............................................................................................................. 41
2.6 Blade vibration measurements ........................................................................................ 43 2.6.1 Strain gauge ............................................................................................................... 43
2.6.2 Blade tip timing (BTT) .............................................................................................. 46
2.6.3 Torsional vibration .................................................................................................... 49
2.6.4 On-bearing vibration (OBV) ..................................................................................... 51
2.6.5 On-casing vibration (OCV) ....................................................................................... 51
2.7 Signal processing techniques ........................................................................................... 52 2.7.1 Time domain analysis ................................................................................................ 52
2.7.2 Frequency domain analysis ....................................................................................... 53
2.7.3 Short time Fourier transform (STFT) ........................................................................ 53
2.7.4 Instantaneous angular speed (IAS) ............................................................................ 53
2.7.5 Order tracking analysis (OTA) .................................................................................. 54
2.7.6 Time synchronising averaging (TSA) ....................................................................... 54
2.8 Research plan to investigate blades’ health using alternative vibration measurements
............................................................................................................................................ 55
2.9 Summary ........................................................................................................................... 55
CHAPTER 3 RIG DESIGN AND EXPERIMENTAL SETUP ..................................... 57
3.1 Introduction ...................................................................................................................... 57
3.2 Finite element modelling .................................................................................................. 57
TABLE OF CONTENTS
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3.3 Primary design model ...................................................................................................... 58
3.4 Final optimised model ...................................................................................................... 63
3.5 Manufacturing of the experimental rig .......................................................................... 66 3.5.1 The experimental rig .................................................................................................. 66
3.5.2 Parts of the experimental rig ...................................................................................... 67
3.5.2.1 Motor ................................................................................................................ 67
3.5.2.2 Shaft .................................................................................................................. 68
3.5.2.3 Bladed disc ........................................................................................................ 68
3.5.2.4 Ball bearing ....................................................................................................... 70
3.5.2.5 Flexible coupling .............................................................................................. 71
3.5.2.6 Foundation table ............................................................................................... 72
3.5.2.7 Rig casing ......................................................................................................... 73
3.5.3 Instrumentation .......................................................................................................... 74
3.5.3.1 Incremental rotary encoder ............................................................................... 74
3.5.3.2 Optical tacho sensor .......................................................................................... 76
3.5.3.3 Tachometer screen ............................................................................................ 76
3.5.3.4 Speed controller ................................................................................................ 77
3.5.3.5 Data acquisition ................................................................................................ 78
3.5.3.6 Accelerometer ................................................................................................... 79
3.5.3.7 Signal conditioning unit .................................................................................... 79
3.5.3.8 Measurement scheme ........................................................................................ 80
3.6 Modal testing .................................................................................................................... 81 3.6.1 Long and short blades FRF ........................................................................................ 82
3.7 Summary ........................................................................................................................... 85
CHAPTER 4 BLADE DIAGNOSIS USING ON-BEARING VIBRATION (OBV) .... 86
4.1 Introduction ...................................................................................................................... 86
4.2 Blade faults simulation..................................................................................................... 86 4.2.1 Healthy with mistuned effect ..................................................................................... 88
4.2.2 Root looseness ........................................................................................................... 88
4.2.3 Crack simulation ........................................................................................................ 89
4.3 Computation of engine order tracking (EOT) ............................................................... 93
4.4 Long blades experiments and data analysis ................................................................... 98 4.4.1 Data analysis .............................................................................................................. 98
4.4.2 Observations and results .......................................................................................... 100
4.5 Short blades experiments and data analysis ................................................................ 104 4.5.1 Experimental setup .................................................................................................. 104
4.5.2 Data analysis ............................................................................................................ 104
4.5.3 Observations and results .......................................................................................... 105
4.6 Summary ......................................................................................................................... 109
CHAPTER 5 BLADE DIAGNOSIS USING ON-CASING VIBRATION (OCV) ..... 110
5.1 Introduction .................................................................................................................... 110
5.2 Experiments on short blades ......................................................................................... 110 5.2.1 Data analysis ............................................................................................................ 110
5.2.2 Observations and results .......................................................................................... 113
5.3 Summary ......................................................................................................................... 117
CHAPTER 6 BLADE DIAGNOSIS USING SHAFT TORSIONAL VIBRATION
(IAS) .................................................................................................................... 118
6.1 Introduction .................................................................................................................... 118
6.2 Instantaneous angular speed (IAS) measurement method ......................................... 118
6.3 Experiments conducted .................................................................................................. 121
6.4 Data analysis ................................................................................................................... 122
6.5 Results of the experiments on long blades .................................................................... 124
6.6 Results of the experiments on short blades .................................................................. 135
TABLE OF CONTENTS
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6.7 Observations and results ............................................................................................... 137
6.8 Summary ......................................................................................................................... 138
CHAPTER 7 COMPARATIVE STUDY BETWEEN OBV, OCV AND IAS
METHODS .................................................................................................................... 140
7.1 Introduction .................................................................................................................... 140
7.2 Results and observations ............................................................................................... 144 7.2.1 Comparison of the three measurements for monitoring short blades ...................... 144
7.2.2 Comparison of the two measurements for monitoring long blades ......................... 145
7.2.3 Final conclusion ....................................................................................................... 145
7.3 Summary ......................................................................................................................... 146
CHAPTER 8 DEVELOPMENT OF POLAR PLOT PRESENTATION (P3)
METHOD .................................................................................................................... 147
8.1 Introduction .................................................................................................................... 147
8.2 Signal processing concept developed ............................................................................ 147 8.2.1 Filter for each blade resonance related to the engine orders.................................... 147
8.2.2 Combining engine orders......................................................................................... 148
8.2.3 Time synchronous averaging for each engine order related to blade resonance ..... 148
8.3 Polar plot ......................................................................................................................... 149 8.3.1 Polar plot diagnosis approach .................................................................................. 149
8.4 Polar plot presentation (P3) for long blades ................................................................ 151 8.4.1 Encoder data analysis .............................................................................................. 152
8.4.2 Encoder data observations and discussion ............................................................... 158
8.4.3 On-bearing data analysis ......................................................................................... 158
8.4.4 On-bearing data observations and discussion .......................................................... 163
8.5 Polar plot presentation (P3) for short blades ............................................................... 163 8.5.1 Encoder data ............................................................................................................ 164
8.5.2 Encoder data observations and discussion ............................................................... 169
8.5.3 On-bearing data analysis ......................................................................................... 169
8.5.4 On-bearing data observations and discussion .......................................................... 174
8.5.5 On-casing data analysis ........................................................................................... 175
8.5.6 On-casing data observations and discussion............................................................ 182
8.6 Summary ......................................................................................................................... 183
CHAPTER 9 MATHEMATICAL MODEL .................................................................. 184
9.1 Introduction .................................................................................................................... 184
9.2 Simple mathematical model .......................................................................................... 184
9.3 Crack simulation ............................................................................................................ 185
9.4 Equation of motion ......................................................................................................... 186 9.4.1 Shaft forces components .......................................................................................... 186
9.4.2 Blades forces components ....................................................................................... 188
9.4.3 The unbalancing disc force components.................................................................. 188
9.5 Dynamic equations of the mathematical model ........................................................... 189 9.5.1 Derivation of the matrices system ........................................................................... 190
9.6 Results and observations ............................................................................................... 197
9.7 Summary ......................................................................................................................... 201
CHAPTER 10 CONCLUSIONS AND FUTURE WORK ............................................. 203
10.1 Overview ......................................................................................................................... 203
10.2 Review of project objectives, achievements and contributions .................................. 204
10.3 Overall conclusion .......................................................................................................... 207
10.4 Novel features ................................................................................................................. 207
10.5 Future work .................................................................................................................... 208
APPENDICES ......................................................................................................................... 209
REFERENCES ........................................................................................................................ 219
LIST OF FIGURES
5
LIST OF FIGURES
Figure 1.1: Typical photograph of low pressure steam turbine rotor with bladed disc stages [2]
............................................................................................................................................ 23
Figure 1.2 : Turbofan engine, Rolls-Royce Trent-900 [3] .......................................................... 24
Figure 1.3: General usage of various techniques in the machinery condition monitoring [1] .... 24
Figure 1.4: Typical turbo-machinery rotating components [7] ................................................... 25
Figure 1.5 : Blade faults due to various operation conditions: (a) crack in steam turbine
blades[8], (b) Pitting and corrosion in steam turbine blades [9], (c) erosion in steam turbine
blades [10], (d) corrosion in steam turbine blades [11], (e) rubbing in tip of turbine blades
[12], (f) blades failure due to deficit in thermal compensation [13] ................................... 27
Figure 1.6: The complex shape of turbo-machinery blade: (a) front view, (b) back view [14] .. 28
Figure 2.1: A schematic of vibration condition monitoring transducers system on turbo-
generator set [20] ................................................................................................................ 35
Figure 2.2: A turbo-generator: a steam turbine bladed system [29]. .......................................... 36
Figure 2.3: Blade failure of a gas turbine due to blade root looseness (adapted from Hee et al.,
2014) [31] ........................................................................................................................... 38
Figure 2.4: A typical crack on a blade in a steam turbine (adapted from Hahn and Sinha, 2015)
[42] ...................................................................................................................................... 39
Figure 2.5: Blade failure in a gas turbine due to rubbing (adapted from Hee et al., 2014) [31] . 41
Figure 2.6: Failure mode distribution in jet engines (adapted from Cowles, 1989) [56] ............ 42
Figure 2.7: Distribution of HCF on jet engine components (adapted from Cowles, 1989) [56] 42
Figure 2.8: Foil strain gauges: (a) schematic of foil gauge (reproduced from Russhard, 2015)
[59] (b) sensor attached on the object [65] ......................................................................... 45
Figure 2.9: Typical mounting of strain gauges: (a) on bladed discs, (b) on blades (a and b
reproduced from Russhard, 2015) [59], and (c) on a steam turbine blade (Reproduced from
Hahn and Sinha, 2013) [66] ................................................................................................ 45
Figure 2.10: Schematic of a two-coil inductive sensor for blade tip clearance measurement
(Reproduced from Li et al., 2014) [76] ............................................................................... 47
Figure 2.11: Non-contact blade vibration tip timing measurement system (Reproduced from
Tamura et al., 2014) [77] .................................................................................................... 47
Figure 2.12: Typical mounting of BTT probes system on a turbine casing for BHM of last stage
blades (Hahn and Sinha, 2013) [66].................................................................................... 48
Figure 2.13: Frequency spectrum of measured torsional vibration signal: (a) healthy tuned
blades, (b) one faulty blade (mistuned) (adapted from Maynard et al., 2000) [94] ............ 50
Figure 3.1: Typical FE mesh for the shaft-disc-blades model: (a) side view, (b) front view ..... 59
Figure 3.2: Typical FE mesh for shaft-disc-blades with blade tip mass model: (a) side-view with
bearings location, (b) front view ......................................................................................... 60
Figure 3.3: Cylindrical blades: (a) blade with blade tip mass, (b) blade without blade tip mass,
(c) crack on blade 20% of blade diameter ........................................................................... 61
Figure 3.4: Experimental rig with cylindrical blades .................................................................. 62
Figure 3.5: Blade fault diagnosis using cylindrical blades: (a) on test rig, (b) bladed disc [90] . 62
Figure 3.6: Five magnets with an attached ring opposite the cylindrical blades ........................ 63
Figure 3.7: FE model of long blades’firstnaturalfrequencywithmatchedmodeshape........... 65
Figure 3.8:FEMofshortblades’firstnaturalfrequencywithmatchedmodeshape ................. 65
Figure 3.9: Photograph of the experimental rig with rectangular blades .................................... 66
Figure 3.10: Electrical motor ...................................................................................................... 68
Figure 3.11: Bladed discs: (a) Long blades, (b) Short blades ..................................................... 69
Figure 3.12: Photograph of a long blade and a short blade ......................................................... 70
Figure 3.13: Pedestal ball bearing unit........................................................................................ 70
Figure 3.14: Flexible coupling: (a) integral clamp attachment methods, (b) Unequal diameter
LIST OF FIGURES
6
shafts [125].......................................................................................................................... 71
Figure 3.15: Flexible coupling installation with the motor shaft and the end of the rotor .......... 72
Figure 3.16: Rig foundation table ............................................................................................... 73
Figure 3.17: Rig safety cover with (a) Side monitoring windows, (b) Top monitoring windows,
(c) Electrical safety switch .................................................................................................. 74
Figure 3.18: Location of encoder at the end of the rotor ............................................................ 75
Figure 3.19: Rotary shaft encoder ............................................................................................... 75
Figure 3.20: Optical tacho sensor ............................................................................................... 76
Figure 3.21: Tachometer screen .................................................................................................. 77
Figure 3.22: Speed controller ...................................................................................................... 78
Figure 3.23: National Instruments NI USB-6221 board ............................................................. 78
Figure 3.24: Accelerometer ........................................................................................................ 79
Figure 3.25: Front and back panel of 4-Channel signal conditioner PCB Piezotronics, 482C ... 79
Figure 3.26: Schematic diagram of the rig data acquisition system ........................................... 81
Figure 3.27: Schematic diagram of blades modal testing ........................................................... 83
Figure 3.28: Photograph of modal testing setup ......................................................................... 83
Figure 3.29: Typical FRF plots for a long blade (blade 4) first natural frequency at 127.50 Hz:
(a) Amplitude, (b) Phase, (c) Imaginary part ...................................................................... 84
Figure 3.30: Typical FRF plots for a short blade (blade 8) first natural frequency at 234.90 Hz:
(a) Amplitude, (b) Phase, (c) Imaginary part ...................................................................... 84
Figure 4.1: Schematic diagram of the blade positions with respect to the tacho sensor ............. 88
Figure 4.2: Simulation of long blade root looseness fault .......................................................... 89
Figure 4.3: Long blade crack simulation: (a) front side of a crack, (b) back side of a crack, (c) a
very thin metal sheet in the crack, and (d) cracked blade location on the bladed disc on the
rig ........................................................................................................................................ 90
Figure 4.4: Short blade crack simulation: (a) crack dimensions, (b) cracked blade with shim
location on the bladed disc on the rig, (c) shim on crack adhesive side and (d) shim on
crack non-adhesive side ...................................................................................................... 90
Figure 4.5: Cracks on the blades at different locations: (a) long blades (b) short blades ........... 91
Figure 4.6: Crack on one long blade on the bladed disc ............................................................. 92
Figure 4.7: Crack on two short blades on the bladed disc .......................................................... 92
Figure 4.8: Tachometer signals: (a) rotating shaft speed, (b) blades EO1 of run-up speed (1X),
and (c) zoom view of (a) ..................................................................................................... 94
Figure 4.9: Measured vibration signal: (a) acceleration raw signal and (b) zoomed view of (a) 95
Figure 4.10: Typical contour plot of speed orders for on-bearing vibration response in horizontal
direction .............................................................................................................................. 96
Figure 4.11: Typical measured on-bearing vibration data for engine order spectra of EO5 of
healthy blades...................................................................................................................... 97
Figure 4.12: Accelerometer location in relation to the on bearing pedestals in the vertical and
horizontal directions ............................................................................................................ 99
Figure 4.13:Typicalamplitudespectrumforlongblades ........................................................... 99
Figure 4.14: Measuredon-bearingaccelerationdataforengineorder spectra of EO5 for healthy
blades with mistuned effects (Case 1)............................................................................... 101
Figure 4.15: Measuredon-bearingaccelerationdataforengineorderspectra of EO5 for blade
looseness (Case2 (ii): Blade no. 5) .................................................................................... 101
Figure 4.16: Measured on-bearing acceleration data for engine order spectra of EO5 for two
cracked blades (Case3 (ii): Blades no. 2 and 4) ................................................................ 102
Figure 4.17: Measured on-bearing acceleration data for engine order spectra of EO10 for
healthy blades (Case1) ...................................................................................................... 102
Figure 4.18: Measuredon-bearingaccelerationdataforengineorder spectra of EO10 for blade
looseness (Case 2 (ii): Blade no. 5) ................................................................................... 103
Figure 4.19: Measuredon-bearingaccelerationdataforengineorderspectra of EO10 for two
LIST OF FIGURES
7
cracked blades (Case 3 (ii): Blade no. 2 and 4) ................................................................ 103
Figure 4.20: Typical measured on-bearing acceleration signals at machine run-up from 600 to
1800 rpm ........................................................................................................................... 104
Figure 4.21: Measured on-bearing acceleration data for engine order spectra of EO10 for
healthybladeswithmistunedeffects(Case 1) .................................................................. 106
Figure 4.22:Case3 (i): measuredon-bearingaccelerationdataforengineorderspectraofEO10
foronecrackedblade(Case 3 (i): Blade no. 4)................................................................. 106
Figure 4.23:Measuredon-bearingaccelerationdataforengineorder spectraofEO10 for two
crackedblades(Case 3 (ii): Blades no. 2 and 4) ............................................................... 107
Figure 4.24: Measured on-bearing acceleration data for engine order spectra of EO20 for
healthybladeswithmistunedeffects(Case 1) .................................................................. 107
Figure 4.25:Case3 (i): measuredon-bearingaccelerationdataforengineorderspectraofEO20
foronecrackedblade(Case 3 (i): Blade no. 4)................................................................. 108
Figure 4.26: Measuredon-bearingaccelerationdataforengineorder spectraofEO20 for two
crackedblades(Case 3 (ii): Blades no. 2 and 4) ............................................................... 108
Figure 5.1:Aschematicofon-casingmeasurementsetup:(a)sideviewand(b)topviewofthe
rig ...................................................................................................................................... 111
Figure 5.2:On-casingmeasurementsetup: (a)topviewofrigshowing thebladesandtheon-
casingaccelerometer(b)rigbacksidecasing .................................................................... 112
Figure 5.3: Typical on-casing measured acceleration data during machine run-up speed (600 to
1800 rpm) .......................................................................................................................... 113
Figure 5.4: On-casing measured acceleration data for engine order EO10 spectra for healthy
blades with mistuned effects (Case 1)............................................................................... 114
Figure 5.5: On-casing measured acceleration data for engine order EO10 spectra for a crack on
one blade (Case 3(i)) ......................................................................................................... 115
Figure 5.6: On-casing measured acceleration data for engine order EO10 spectra for cracks on
two blades (Case 3(ii)) ...................................................................................................... 115
Figure 5.7: On-casing measured acceleration data for engine order EO20 spectra for healthy
blades with mistuned effects (Case 1)............................................................................... 116
Figure 5.8: On-casing measured acceleration data for engine order EO20 spectra for a crack on
one blade (Case 3(i)) ......................................................................................................... 116
Figure 5.9: On-casing measured acceleration data for engine order EO20 spectra for cracks on
two blades (Case 3(ii)) ...................................................................................................... 117
Figure 6.1: Schematic of the encoder pulse train ...................................................................... 119
Figure 6.2: Measured encoder pulse train of the rotating shaft ................................................. 119
Figure 6.3: Ideal shaft IAS showing a constant shaft speed without torsion ............................ 121
Figure 6.4: Typical shaft IAS showing the possibility of shaft torsion .................................... 121
Figure 6.5: A typical rotor speed profile for the machine during run-up .................................. 122
Figure 6.6: Typical magnified measured encoder raw signals .................................................. 122
Figure 6.7: A typical IAS response at e.g. EO5 for Case 1: healthy blades illustrating frequency
modes ................................................................................................................................ 123
Figure 6.8: A typical IAS response at EO10 for short blades; Case 1: Healthy blades ............ 124
Figure 6.9: Measured encoder (IAS) data for engine order EO5 spectra for healthy blades (Case
1) ....................................................................................................................................... 125
Figure 6.10: Measured encoder (IAS) data for engine order EO5 spectra for blade root looseness
(Case 2(i): Blade no. 1) ..................................................................................................... 125
Figure 6.11: Measured encoder (IAS) data for engine order EO5 spectra for blade root looseness
(Case 2(ii): Blade no. 5) .................................................................................................... 126
Figure 6.12: Measured encoder (IAS) data for engine order EO5 spectra for a crack on one
blade (Case 3(i): Blades no. 4) .......................................................................................... 126
Figure 6.13: Measured encoder (IAS) data for engine order EO5 spectra for cracks on two
LIST OF FIGURES
8
blades (Case 3(ii): Blades no. 2 and 4) ............................................................................. 127
Figure 6.14: Measured encoder (IAS) data for engine order EO5 spectra for a crack on one
blade (Case 3(iii): Blade 2) ............................................................................................... 127
Figure 6.15: Measured encoder (IAS) data for engine order EO5 spectra for a crack on one
blade after 100 minutes of machine operation (Case 3(iv): Blade 4)................................ 128
Figure 6.16: Measured encoder (IAS) data for engine order EO10 spectra for healthy blades
(Case 1) ............................................................................................................................. 128
Figure 6.17: Measured encoder (IAS) data for engine order EO10 spectra for blade root
looseness (Case 2(i): Blade no. 1) ..................................................................................... 129
Figure 6.18: Measured encoder (IAS) data for engine order EO10 spectra for blade root
looseness (Case 2(ii): Blade no. 5) .................................................................................... 129
Figure 6.19: Measured encoder (IAS) data for engine order EO10 spectra for a crack on one
blade (Case 3(i): Blade no. 4) ........................................................................................... 130
Figure 6.20: Measured encoder (IAS) data for engine order EO10 spectra for cracks on two
blades (Case 3(ii): Blades no. 2 and 4) ............................................................................. 130
Figure 6.21: Measured encoder (IAS) data for engine order EO10 spectra for a crack on one
blade (Case 3(iii): Blade no. 2) ......................................................................................... 131
Figure 6.22: Measured encoder (IAS) data for engine order EO10 spectra for a crack on one
blade after 100 minutes of machine operation (Case 3(iv): Blade no. 4).......................... 131
Figure 6.23: Measured encoder (IAS) data for engine order EO15 spectra for healthy blades
(Case 1) ............................................................................................................................. 132
Figure 6.24: Measured encoder (IAS) data for engine order EO15 spectra for blade root
looseness (Case 2(i): Blade no. 1) ..................................................................................... 132
Figure 6.25: Measured encoder (IAS) data for engine order EO15 spectra for blade root
looseness (Case 2(ii): Blade no. 5) .................................................................................... 133
Figure 6.26: Measured encoder (IAS) data for engine order EO15 spectra for a crack on one
blade (Case 3(i): Blade no. 4) ........................................................................................... 133
Figure 6.27: Measured encoder (IAS) data for engine order EO15 spectra for cracks on two
blades (Case 3(ii): Blades no. 2 and 4) ............................................................................. 134
Figure 6.28: Measured encoder (IAS) data for engine order EO15 spectra for a crack on one
blade (Case 3(iii): Blade no. 2) ......................................................................................... 134
Figure 6.29: Measured encoder (IAS) data for engine order EO15 spectra for a crack on one
blade after 100 minutes of machine operation (Case 3(iv): Blade no. 4).......................... 135
Figure 6.30: Measured encoder (IAS) data for engine order EO10 spectra for healthy blades
(Case 1) ............................................................................................................................. 136
Figure 6.31: Measured encoder (IAS) data for engine order EO10 spectra for a crack on one
blade (Case 3(i): Blade no. 5) ........................................................................................... 136
Figure 6.32: Measured encoder (IAS) data for engine order EO10 spectra for cracks on two
blades (Case 3(ii): Blades no. 5 and 7) ............................................................................. 137
Figure 7.1: A schematic flowchart for the processes that are followed to select the best
measurement for BHM of rotating machines .................................................................... 140
Figure 8.1 Typical sample of the polar plot presentation model ............................................... 150
Figure 8.2: Mean speed of EO5 for IAS data from healthy long blades ................................... 152
Figure 8.3: Polar plot of IAS measured data of EO5 for cases: (a) healthy blades, (b) blade root
looseness, (c) a crack on one blade and (d) cracks on two blades .................................... 153
Figure 8.4: Polar plot of IAS measured data of EO10 for cases: (a) healthy blades, (b) blade root
looseness, (c) crack on one blade and (d) cracks on two blades ....................................... 154
Figure 8.5: Polar plot of IAS measured data of EO15 for cases: (a) healthy blades, (b) blade root
looseness, (c) crack on one blade and (d) cracks on two blades ....................................... 155
Figure 8.6: Polar plot of IAS measured data of EO5+EO10 for cases: (a) healthy blades, (b)
blade root looseness, (c) crack on one blade and (d) cracks on two blades ...................... 156
LIST OF FIGURES
9
Figure 8.7: Polar plot of IAS measured data of EO5+EO10+EO15 for cases: (a) healthy blades,
(b) blade root looseness, (c) crack on one blade and (d) cracks on two blades ................ 157
Figure 8.8: Polar plot of acceleration (OBV) measured data of EO15 for cases: (a) healthy
blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two blades .... 159
Figure 8.9: Polar plot of acceleration (OBV) measured data of EO5+EO10 for cases: (a) healthy
blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two blades .... 160
Figure 8.10: Polar plot of acceleration (OBV) measured data of EO5+EO10+EO15 for cases: (a)
healthy blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two
blades ................................................................................................................................ 161
Figure 8.11: Polar plot of acceleration (OBV) measured data of EO10+EO20 for cases: (a)
healthy blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two
blades ................................................................................................................................ 162
Figure 8.12: Polar plot of IAS measured data of EO10 for cases: (a) healthy blades, (b) crack on
one blade and (c) cracks on two blades ............................................................................. 165
Figure 8.13: Polar plot of IAS measured data of EO20 for cases: (a) healthy blades, (b) crack on
one blade and (c) cracks on two blades ............................................................................. 166
Figure 8.14: Polar plot of IAS measured data of EO10 + EO20 for cases: (a) healthy blades, (b)
crack on one blade and (c) cracks on two blades .............................................................. 167
Figure 8.15: Polar plot of IAS measured data of EO10+EO30 for cases: (a) healthy blades, (b)
crack on one blade and (c) cracks on two blades .............................................................. 168
Figure 8.16: Polar plot of acceleration (OBV) measured data of EO10+EO20 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades ............................... 170
Figure 8.17: Polar plot of acceleration (OBV) measured data of EO10+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades ............................... 171
Figure 8.18: Polar plot of acceleration (OBV) measured data of EO20+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades ............................... 172
Figure 8.19: Polar plot of acceleration (OBV) measured data of EO10+EO20+EO30 for cases:
(a) healthy blades, (b) crack on one blade and (c) cracks on two blades .......................... 173
Figure 8.20: Polar plot of acceleration (OCV) measured data of EO30 for cases: (a) healthy
blades, (b) crack on one blade and (c) cracks on two blades ............................................ 176
Figure 8.21: Polar plot of acceleration (OCV) measured data of EO10+EO30 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades ............................... 177
Figure 8.22: Polar plot of acceleration (OCV) measured data of EO10+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades ............................... 178
Figure 8.23: Polar plot of acceleration (OCV) measured data of EO20+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades ............................... 179
Figure 8.24: Polar plot of acceleration (OCV) measured data of EO20+EO30+EO40 for cases:
(a) healthy blades, (b) crack on one blade and (c) cracks on two blades .......................... 180
Figure 8.25: Polar plot of acceleration (OCV) measured data of EO10+EO20+EO40 for cases:
(a) healthy blades, (b) crack on one blade and (c) cracks on two blades .......................... 181
Figure 9.1: Simplified rotor model with 8 blades ..................................................................... 185
Figure 9.2: Free body diagram for rotor forces ......................................................................... 187
Figure 9.3: Free body diagram of unbalanced mass disc forces in x-y plane ........................... 189
Figure 9.4: Typical acceleration run-up responses signal, (a) Healthy blades, (b) Crack on Blade
.......................................................................................................................................... 198
Figure 9.5: Typical contour plot of BR frequency (1xBR), (a) Healthy blades, (b) Crack on
Blade ................................................................................................................................. 199
Figure 9.6: Typical contour plot of BR frequency (2xBR), (a) Healthy blades, (b) Crack on
Blade ................................................................................................................................. 200
Figure 9.7: Typical estimated acceleration responses at EO5, (a) Healthy blades, (b) Crack on
Blade 2 .............................................................................................................................. 201
LIST OF FIGURES
10
Figure A 1: Combined curves of bearing measured acceleration data for long blades for engine
order EO5 spectra: Case (1) healthy blades, (2-ii) blade 5 root looseness, (3-ii) cracks on
blades 2 & 4 ...................................................................................................................... 209
Figure A 2: Combined curves of bearing measured acceleration data for long blades for engine
order EO10 spectra: Case (1) healthy blades, (2-ii) blade 5 root looseness, (3-ii) cracks on
blades 2 & 4 ...................................................................................................................... 210
Figure A 3: Combined curves of bearing measured acceleration data for short blades for engine
order EO10 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4 ...................................................................................................................... 211
Figure A 4: Combined curves of bearing measured acceleration data of short blades for engine
order EO20 spectra for: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4 ...................................................................................................................... 212
Figure B 1: Combined curves of casing measured acceleration data for short blades for engine
order EO10 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4 ...................................................................................................................... 213
Figure B 2: Combined curves of casing measured acceleration data for short blades for engine
order EO20 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4 ...................................................................................................................... 214
Figure C 1: Combined curves of measured encoder (IAS) data for engine order EO5 spectra:
Case (1) healthy blades, Case (2-i) blade 1 root looseness, Case (2-ii) blade 5 root
looseness, (3-i) crack on blade 4, (3-ii) cracks on blades 2 & 4, (3-iii) crack on blade 2, (3-
iv) crack on blade 4 after 100 minute of running .............................................................. 215
Figure C 2: Combined curves of measured encoder (IAS) data for engine order EO10 spectra:
Case (1) healthy blades, Case (2-i) blade 1 root looseness, Case (2-ii) blade 5 root
looseness, (3-i) crack on blade 4, (3-ii) cracks on blades 2 & 4, (3-iii) crack on blade 2, (3-
iv) crack on blade 4 after 100 minute of running .............................................................. 216
Figure C 3: Combined curves of measured encoder (IAS) data for engine order EO15 spectra:
Case (1) healthy blades, Case (2-i) blade 1 root looseness, Case (2-ii) blade 5 root
looseness, (3-i) crack on blade 4, (3-ii) cracks on blades 2 & 4, (3-iii) crack on blade 2, (3-
iv) crack on blade 4 after 100 minute of running .............................................................. 217
Figure C 4: Combined curves of measured encoder (IAS) data of short blades for engine order
EO10 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on blades 2 & 4
.......................................................................................................................................... 218
LIST OF TABLES
11
LIST OF TABLES
Table 3-1: Shaft-disk-blades model dimensions ......................................................................... 58
Table 3-2: Model properties and dimensions of the shaft-disc-blades system including design of
blade tip mass ...................................................................................................................... 60
Table 3-3: Shaft–disc–blade(s) data for long and short blades ................................................... 64
Table 3-4: Specification of the motor ......................................................................................... 67
Table 3-5: Specification of the encoder ...................................................................................... 75
Table 3-6: Specifications of the Tacho sensor ............................................................................ 76
Table 3-7: Specifications of the speed controller CL750 ........................................................... 77
Table 3-8: Experimentally identified long and short blades first natural frequency ................... 82
Table 4-1: Cases of blade faults conditions ................................................................................ 87
Table 7-1 : Comparison between the results of OBV, OCV and IAS for short blades ............. 141
Table 7-2: Comparison between the results of OBV and IAS for long blades ......................... 143
Table 7-3: Articles published related to this study in the research area of blade vibration fault
detection using different measurements ............................................................................ 146
Table 8-1: The numbers of single leaves and coupled leaves in the polar plot of EO5 for the four
cases of blade health conditions based on IAS data .......................................................... 153
Table 8-2: The numbers of single leaves and coupled leaves in the polar plot of EO10 for the
four cases of blades health conditions based on IAS data ................................................ 154
Table 8-3: The numbers of single leaves and coupled leaves in the polar plot of EO15 for the
four cases of blades health conditions based on IAS data ................................................ 155
Table 8-4: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO5+EO10 for the four cases of blades health conditions based on IAS data ................. 156
Table 8-5: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO5+EO10+EO15 for the four cases of blades health conditions using IAS data ........... 157
Table 8-6: The numbers of single leaves and coupled leaves in the polar plot of EO15 for the
four cases of blade health conditions using OBV data ..................................................... 159
Table 8-7: The numbers of single leaves and coupled leaves in the polar plot of a combination
of EO5+EO10 for the four cases of blade health conditions using OBV data .................. 160
Table 8-8: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO5+EO10+EO15 for the four cases of blade health conditions using OBV data........... 161
Table 8-9: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+EO20 for the four cases of blade health conditions using OBV data .................... 162
Table 8-10: The numbers of single leaves and coupled leaves in the polar plot of EO10 for the
three cases of blade health conditions using IAS data ...................................................... 165
Table 8-11: The numbers of single leaves and coupled leaves in the polar plot of EO20 for the
three cases of blade health conditions using IAS data ...................................................... 166
Table 8-12: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+E20 for the three cases of blade health conditions using IAS data ........................ 167
Table 8-13: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+E30 for the three cases of blade health conditions using IAS data ........................ 168
Table 8-14: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+E20 for the three cases of blade health conditions using OBV data ...................... 170
Table 8-15: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+E40 for the three cases of blade health conditions using OBV data ...................... 171
Table 8-16: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO20+E40 for the three cases of blade health conditions using OBV data ...................... 172
Table 8-17: The numbers of single leaves and coupled leaves in the polar plot of combination of
LIST OF TABLES
12
EO10+EO20+E30 for the three cases of blade health conditions using OBV data .......... 173
Table 8-18: The numbers of single leaves and coupled leaves in the polar plot of EO30 for the
three cases of blade health conditions using OCV data .................................................... 176
Table 8-19: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+E30 for the three cases of blade health conditions using OCV data ...................... 177
Table 8-20: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+E40 for the three cases of blade health conditions using OCV data ...................... 178
Table 8-21: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+E40 for the three cases of blade health conditions using OCV data ...................... 179
Table 8-22: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO20+EO30+E40 for the three cases of blade health conditions using OCV data .......... 180
Table 8-23: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+EO20+E40 for the three cases of blade health conditions using OCV data .......... 181
Table 9-1: Rotor/ blade parameters used in the mathematical model ....................................... 185
Table 9-2: First mode shape frequencies for blades and shaft obtained using the mathematical
model ................................................................................................................................ 190
LIST OF NOMENCLATURES
13
LIST OF NOMENCLATURES
E Young’smodulus
density (kg/m3)
Rpm revolution per minute
Rps revolution per second
M Mass
A Acceleration
F Force
K Stiffness
C Damping
mb blade mass
kb blade stiffness
cb blade damping
xb blade acceleration
xb blade velocity
xb blade displacement
mr rotor mass
kr rotor stiffness
cr rotor damping
xr rotor acceleration
xr rotor velocity
xr rotor displacement
mu unbalancing mass
R Radius
𝜔 angular velocity
Fimb imbalanced force
F frequency (Hz)
[M] mass matrix
[C] damping matrix
[K] stiffness matrix
{𝐹𝑢𝑛𝑏} unbalanced force vector
{X} acceleration vector
LIST OF NOMENCLATURES
14
{X} velocity vector
{X} displacement vector
t time (s)
tv amplitude of signal (V)
x distance (m)
LIST OF ABBREVIATIONS
15
LIST OF ABBREVIATIONS
AC Alternating Current
BHM Blade Health Monitoring
BPF Blade Pass Frequency
BR Blade Resonance
BTT Blade Tip Time
CBM Condition Based Monitoring
CFD Computational Fluid Dynamics
CM Condition Monitoring
DC Direct Current
Di Inner Diameter
Do Outer Diameter
EO,EO’s Engine Order(s)
EOT Engine Order Tracking
FE Finite Element
FEA Finite Element Analysis
FEM Finite Element Modelling
FFT Fast Fourier Transform
FOD Foreign Object Damage
FRF Frequency Response Function
HCF High Cycle Fatigue
IAS Instantaneous Angular Speed
IRM Impulse-Response Modal
LCF Low Cycle Fatigue
LPT Low Pressure Turbine
MACE Mechanical, Aerospace and Civil Engineering
MW Mega Watt
MDOF Multi Degrees Of Freedom
NI National Instruments
OBV On-Bearing Vibration
OCV On-Casing Vibration
OT Order Tracking
LIST OF ABBREVIATIONS
16
OTA Order Tracking Analysis
P3 Polar Plot Presentation
PC Personal Computer
RPM Revaluation Per Minute
RPS Revolution Per Second
SDOF Single Degree Of Freedom
STFT Short Time Fourier Transform
TG Turbo-Generator
TOA Time of Arrival
TSA Time Synchronizing Averaging
ABSTRACT
17
ABSTRACT
Ahmed Gubran - Doctor of Philosophy
The University of Manchester – 09 February 2015
VIBRATION DIAGNOSIS OF BLADES OF ROTATING MACHINES
Rotating blades are considered to be the one of the most common cause of failures in
rotating machinery. Blade failure modes normally occur as a result of cracks due to
unexpected operating conditions, which are normally caused by accidents of foreign
objects damage, high cycle fatigue, blade rubbing, blade root looseness, and degradation
from erosion and corrosion. Thus, detection of blade faults has an important role in
reducing blade related failures and allowing repairs to be scheduled for the machinery.
This in turn will lead to reduction in maintenance costs and thus raise productivity and
safety aspects of operation. To maintain vital components of rotating machines, such as
blades, shafts, bearings and gear boxes, at optimal levels, detection of failures in such
components is important, because this will prevent any serious damage that could affect
performance.
This research study involves laboratory tests on a small rig with a bladed disc rotor that
applied vibration measurements and analysis for blade fault detection. Three
measurements: shaft torsional vibration, on-bearing vibration (OBV) and on-casing
vibration (OCV), are used. A small test rig of a single stage bladed disc holding 8-
blades was designed and manufactured, to carry out this research study to assess the
usefulness and capability of each vibration technique in detection of incipient defects
within machine blades.
A series of tests was conducted on a test rig for three different cases of blade health
conditions: (a) healthy blade(s) with mistuned effects, (b) blade root looseness and (c)
cracks in a blade on two different blade sizes (long and short blades) in order to
discover changes in blades’ dynamic behaviour during the machine running-up
operation. The data were collected using the three measurements during machine run-up
andthenrecorded.Themeasuredvibrationdatawereanalysedbycomputingtheblades’
resonance at different engine orders (EOs) related to the blade(s) resonance frequencies
and their higher harmonics, to understand the blade(s) dynamics behaviour for the cases
of healthy and faulty blade(s).
Data have been further processed using a polar plot presentation method which provides
clear results that can be used for monitoring blade integrity. To validate the obtained
experimental results, a simplified mathematical model was also developed. Finally, a
comparative study between three methods was undertaken to understand the relative
advantages and limitations in the blade heath monitoring.
Keywords: Blade vibration, Blade health monitoring, Shaft torsional vibration, On-
bearing vibration (OBV), On-casing vibration (OCV), Blade faults, Engine orders
(EOs), Polar plot presentation, Order tracking (OT).
DECLARATION
18
DECLARATION
I hereby declare that no portion of the work referred to in the thesis has been submitted
in support of an application for another degree or qualification of this or any other
university or other institute of learning.
COPYRIGHT STATEMENT
19
COPYRIGHT STATEMENT
The following four notes on copyright and the ownership of intellectual property rights
must be included as written below:
i. The author of this thesis (including any appendices and/or schedules to this thesis)
owns certain copyright or related rightsinit(the“Copyright”)ands/hehasgivenThe
University of Manchester certain rights to use such Copyright, including for
administrative purposes.
ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic
copy, may be made only in accordance with the Copyright, Designs and Patents Act
1988 (as amended) and regulations issued under it or, where appropriate, in accordance
with licensing agreements which the University has from time to time. This page must
form part of any such copies made.
iii. The ownership of certain Copyright, patents, designs, trade marks and other
intellectual property (the “Intellectual Property”) and any reproductions of copyright
works in the thesis, for example graphs and tables (“Reproductions”),whichmay be
described in this thesis, may not be owned by the author and may be owned by third
parties. Such Intellectual Property and Reproductions cannot and must not be made
available for use without the prior written permission of the owner(s) of the relevant
Intellectual Property and/or Reproductions.
iv. Further information on the conditions under which disclosure, publication and
commercialisation of this thesis, the Copyright and any Intellectual Property and/or
Reproductions described in it may take place is available in the University IP Policy
(see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=487), in any relevant
Thesis restriction declarations deposited in the University Library, The University
Library’sregulations(seehttp://www.manchester.ac.uk/library/aboutus/regulations) and
inTheUniversity’spolicy on Presentation of Theses.
ACKNOWLEDGEMENNTS
20
ACKNOWLEDGEMENTS
First of all, my ultimate thanks are to Allah almighty who created me and gave me
strength and knowledge.
Also, I would like to express my appreciation and sincere gratitude to my supervisor,
Dr Jyoti K. Sinha, for his continuous support, supervision, and guidance over the entire
period of this research study.
I would also like to express my gratitude to the school of MACE postgraduate
programme administrator Ms Beverley Knight for her diligent and positive efforts in
ensuring my PhD runs smoothly.
My thanks go to the school technicians who have helped me to manufacture and
assemble the test rig and especially to the technical coordinators at the Pariser Building
workshop, Mr Paul Townsend and Mr Phil Oakes, for their assistance in preparing
the test rig manufacture.
Special thanks go also to my uncle Abdullah Jubran for his unlimited support and
encouragement, and also to my brothers, Mahmud and Abdulhameed, for all their
immense support and also to all my other brothers and sisters.
Specially and most importantly, I would like to thank my wife Aisha for her support,
patience, encouragement and sharing every moment throughout my PhD in great love
and support. Also, my thanks go to my beloved daughter Aziza and beloved sons
Abdurrahman and Abdullah.
I would also like to express my gratitude to all my friends and colleagues for making
these years in Manchester very special and extremely pleasant.
Finally, my special thanks are dedicated to the Libyan Government for granting me a
scholarship for my PhD study.
ACKNOWLEDGEMENNTS
21
DEDICATION
I dedicate this work to my mother (died 1987), my father (died 2007), my uncle
Abdullah, my wife, my kids and all my family members who encouraged me to
complete this PhD study.
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
22
CHAPTER 1
INTRODUCTION
1.1 General Introduction
Rotating machinery are commonly integrated with most vital industrial processes such
as power generation, aerospace, mining, food processing, etc. Figure 1.1 and 1.2 show
pictures of rotating machines in a steam turbine and an aero-engine respectively. These
industries face costly failures due to vibration problems in the rotating machinery which
at times result to huge annual financial losses to the companies, including safety
implications. Over the years, the need to eliminate costly (reduced throughput, safety,
environment, customer satisfaction, etc.) downtimes by companies has significantly
tilted attention towards effective machinery faults diagnosis and more proactive
maintenance techniques. A very prominent strategy that has gained significant attention
over the past decades is Condition-based Maintenance (CBM), which basically entails
studying and trending key operational parameters (such as vibration, pressure,
temperature, sound, etc.) of machines during day-to-day operations without necessarily
interrupting production activities. A very vital merit of CBM over other maintenance
strategies (e.g. run-to-failure or planned preventive maintenance) revolves around the
fact that emerging machine faults and abnormalities can be captured very early, before
such faults can lead to catastrophic failures, which in turn enhances the ability of
companies to improve the satisfaction of their companies. In order to adequately
harness all the benefits of a CBM system, it is very vital that this strategy is strictly
limited to the most critical equipment of any company.
During machinery faults diagnosis with CBM, various operating parameters are
captured and tracked, using different monitoring measurements. Amongst all the
conventional CBM techniques (e.g. vibration analysis, lube & wear debris analysis,
infrared thermography, acoustic emission, process monitoring, human senses, etc.),
vibration analysis is the most popular owing to the fact that it offers the longest lead
time to equipment failure. It represents up to 41% of CBM techniques used in rotating
machines monitoring as can be seen in the pie chart in Figure 1.3. The pie chart also
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
23
shows the usage percentages of other CBM techniques for monitoring rotating machines
[1].
Figure 1.1: Typical photograph of low pressure steam turbine rotor with bladed disc stages [2]
Bladed discs Main shaft
Blade
Disc
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
24
Figure 1.2 : Turbofan engine, Rolls-Royce Trent-900 [3]
Figure 1.3: General usage of various techniques in the machinery condition monitoring [1]
Vibration
41%
Tribology
12%
Thermography
5%
Ultrasonics
3%
Motor Analysis
11%
Electrical
Analysis
1%
Process
Parameters
25%
Airborne Noise
2%
Engine
casing
Compressor
blade stages
Exhaust
nozzle
discs
Turbine blade
stages
Fan
blades
Main
shaft
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
25
With vibration in rotating machines been recognized as a major cause of failure, the task
of faults diagnosis in rotating machines through the application of vibration analysis
primarily involves understanding the vibrational behavior of the machine during healthy
and suspected faulty conditions at pre-defined intervals, so as to capture irregularities in
performance, prior to failure. Research in condition monitoring resulted in developing
better diagnostic techniques which have benefited companies by allowing them to save
huge amounts of money [4].
A typical turbo-machinery is composed of shafts, bladed discs, bearings, sleeves,
couplings, gears, housing and foundation as shown in Figure 1.4. However, turbine
blades, which are key components of gas turbines, suffer high mechanical loading due
to extreme changes in both temperature and pressure, which have often been classified
as major sources of failures in many steam turbines and jet engines. Carter [5] reported
that turbine blade damage can also be caused by foreign object damage (FOD) or
torsion forces. Figure 1.5 shows blade failures due to impacts of varying environmental
conditions. The most common cause of failures in gas turbines and jet engines is related
to blade faults, which were reported to represent up to 42% of total gas turbine failures
[6].
Figure 1.4: Typical turbo-machinery rotating components [7]
Blade stages Bladed discs
Turbine
housing Shaft
Disc
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
26
The continuous need for enhanced performance of modern day industrial machines such
as turbines has led to complicated shapes of components such as blades (Figure 1.6),
which significantly increase the difficulty levels of their manufacture The operating
conditions of turbines expose these blades to extreme vibration excitation mechanisms,
thus making it sometimes difficult to measure performance. Despite the advances in
theory and technology of faults detection and diagnosis over the years, effective and
accurate measurement still poses some challenges, which in turn triggers the need for
further improvements on the understanding of blade vibration phenomenon. The lack of
full understanding of the mechanisms of vibration and the challenges associated with
direct measurements of blade vibration and its interaction with other components have
made the diagnostic process very difficult. This has however led to the application of
alternative approaches such as indirect measurement techniques for ascertaining the
vibration of machine components known to have strong dynamic interactions with the
blades.
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
27
(a)
(b)
(c)
(d)
(e)
(f)
Figure 1.5 : Blade faults due to various operation conditions: (a) crack in steam turbine
blades[8], (b) Pitting and corrosion in steam turbine blades [9], (c) erosion in steam
turbine blades [10], (d) corrosion in steam turbine blades [11], (e) rubbing in tip of
turbine blades [12], (f) blades failure due to deficit in thermal compensation [13]
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
28
Figure 1.6: The complex shape of turbo-machinery blade: (a) front view, (b) back view [14]
1.2 Motivation
Rotating machinery is widely used in modern industries and these machines are
complex and have several components that can potentially fail at any stage during
operation, especially under extreme conditions. Since blades represent the main parts
(core) of many industrial rotating machines (e.g. turbo-generators, induced draft fans,
compressors, pumps, etc.), there exists a pressing need to develop an effective and
robust CBM technique that will adequately simplify blade faults diagnosis, so as to
enhance the reliability of bladed rotating machines Nowadays, the most effective
approaches for diagnosing rotating machine faults is vibration based condition
monitoring. Hence, the motivation of this study is to investigate and proposed vibration
based faults diagnosis techniques that can simplify blade health monitoring (BHM),
without necessarily comprising the ability to detect faults early. Owing to the efficiency
gaps in the currently used BHM techniques, there is a significant need for more
effective, non-contact and non-destructive measurements to detect and diagnose blade
health conditions, using advanced signal processing methods. In order to achieve the
objectives of this research, three measurements were investigated, namely; shaft
torsional vibration based-on instantaneous angular speed (IAS), on-bearing vibration
(OBV) and on-casing vibration (OCV). The significance of this research lies in the
development of an advanced signal processing technique to develop a relatively easy
and more effective method for BHM.
(a) (b)
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
29
1.3 Objective
The objectives of the current research are as follows:
Objective 1: To design and manufacture a test rig suitable for simulating rotating
machine blades and to detect blade faults due to mistuned blades, blade root looseness
and blade cracks, using vibration based condition monitoring measurements.
Objective 2: To apply the proposed measurements of shaft torsional vibration, OBV
and OCV during rotating machine running-up and/or running-down in order to examine
their abilities to detect blade faults based on vibration signals.
Objective 3: To compare and analyse the results obtained using the three proposed
measurements, so as to deduce which measurement is the most useful for detecting and
diagnosing blade faults.
Objective 4: To select the best measurement in combination with a unique signal
processing method that will enhance the detection of blade mistuning effects, blade root
looseness and cracked blades.
Objective 5: To compare both theoretical and experimental observations, so as to better
understand blade vibration behaviour.
1.4 Thesis outline
This thesis consists of ten chapters, and the contents of each chapter are summarised as
follows:
Chapter 2 provides a review of the literature pertinent to this research. The chapter
reports earlier studies in the area of rotating machines condition monitoring, with
particular emphasis on blade vibration fault detection, using torsional vibration
measurement, on-bearing and on-casing vibration measurements. The chapter also
provides a highlight of some of the popularly applied blade vibration fault detection
measurements and blade failure modes.
Chapter 3 describes the experimental setup including rig design, rig construction and
the various sensors used in this research. Additionally, a description of the finite
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
30
element (FE) model for the entire rig and its rotating components (shaft-disc-blades)
using ABAQUS code is also provided, and modal analysis for shaft and blades.
Chapter 4 describes the experimental results and the corresponding discussions
relevant to the detection of blade faults (blade root looseness and cracked blade(s))
using the OBV measurement.
Chapter 5 describes the experimental results and corresponding discussion relevant to
the detection of blade faults (cracked blade(s)) using an OCV measurement, which was
based on using acceleration signals data on casing towards the rotating blades disc.
Chapter 6 describes the experimental results and the corresponding discussions
relevant to the detection of blade faults (blade root looseness and cracked blade(s))
using shaft torsional vibration measurement, which was based on the application of
incremental shaft encoder, through the application of IAS signals.
Chapter 7 describes a comparison of the results obtained from the application of all
three measurements (i.e. OBV, OCV and shaft torsional vibration based-on IAS), so as
to determine which of the measurements is the most suitable for BHM.
Chapter 8 discusses a polar plot method of the healthy and faulty blade conditions for
both long and short blades, using the diagnosis results from IAS signals, OBV and OCV
signals. It must be stated that this method is considered a novel technique for the
detection and diagnosis of blade health conditions.
Chapter 9 describes the mathematical model of a single staged bladed disc rotor with
eight blades, which was developed and simulated using transient vibration response
signals of rotating blades. The analytically and numerically modelled blade vibrations
were then verified using experimental data, so as to boost the confidence level of the
present study.
Chapter 10 presents a review of the objectives and achievements of the current
research, in addition to a comprehensive conclusion and areas of future endeavours.
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
31
1.5 Research contribution
The overall conclusion is about improved turbine blades’ condition monitoring
processes for steam turbines and aero-engines using vibration measurements of
torsional vibration based on IAS signals as the specific area in which a contribution to
knowledge is claimed. Also, measurements based on OBV and OCV developed in this
study are highlighted in order to be employed for BHM. The current research has
successfully developed and applied a new signal processing technique that is based on a
polar plot representation of frequency analysis, which can be useful for the diagnosis of
blade faults associated with rotating machines such as steam turbines, gas turbines, etc.
1.6 Publications
The findings of this research study were published in following international journals
and conferences.
1.6.1 Journal publications
1- Gubran A. A. and J. K. Sinha, Shaft instantaneous angular speed for blade vibration
in rotating machine. Mechanical Systems and Signal Processing, 2014. 44(1-2): p.
47-59. (Special Issue on Instantaneous Angular Speed (IAS) Processing and Angular
Applications).
2- Gubran A. A. and J. K. Sinha, A comparison of on-bearing and shaft torsional
vibration for blade vibration. SOP Transactions on Signal Processing, 2014. 1(1):
p.1-9.
1.6.2 Conferences publications
1- Gubran A. A. and J. K. Sinha, Blade vibration: A comparison of on bearing and shaft
torsional vibration. 11th International Conference on Vibration Problems ICOVP,
Lisbon, Portugal, 9-12th September 2013.
CHAPTER 1 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
32
2- Gubran A. A. and J. K. Sinha, Comparison between long and short blade vibration
using shaft instantaneous angular speed in rotating machine. Proceedings for the
ASME: Turbine Technical Conference and Exposition, Dusseldorf, Germany, 16-
20th
June 2014.
3- Gubran A. A. and J. K. Sinha, Comparison of on-bearing and on-casing vibration for
blade vibration. 10th International Conference on Vibration Engineering and
Technology of Machinery VETOMAC-X, Manchester, UK, 9-11th September 2014.
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
This chapter presents a comprehensive literature review summarising the previous
published work in the area of blade vibration condition monitoring of rotating
machinery, as well as different measurement techniques commonly used for the
detection and diagnosis of blade(s) health conditions. The detection of blade faults is
not easy due to the lack of practical direct sensor on the blade during machine operation
tomonitor the blade health condition. The diagnosis of rotating blades’ conditions is
usually conducted using indirect measurements such as vibration techniques which are
widely utilized in rotating machines.
2.2 Condition monitoring in rotating machines
Condition monitoring (CM) is the process of monitoring and observing the conditions
of machines during operation, so as to avoid failures and its consequences. CM often
involves the monitoring of various measured parameters associated with machine
mechanical conditions such as vibration, pressure, temperature, oil debris, and machine
performance in order to determine the machine’s state of health [15]. Machine
malfunction diagnosis using CM has developed over many years due to the increasing
need for running more efficient machines at higher speeds and long operation hours
with less shutdown time, less maintenance cost, high productivity and better safety
considerations. Hence, by applying CM to rotating machines, failures can be prevented
and machine stoppage for unscheduled maintenance can be minimised. Failure occurs
because of many factors (either one at a time or in combination), and these factors are
related to many parameters which may include mode and duration of operation.
Various parameters including vibration, noise, corrosion, fatigue, shock, heat, cold,
dust, humidity, oil debris, temperature, pressure, and speed are known to have a
negative impacts on the health of rotating machines [16]. The use of condition
monitoring allows for the effective scheduling of routine maintenance activities, so as to
prevent catastrophic failures and costly downtimes. Therefore, it has recently become
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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essential to monitor the conditions of turbo-machinery so as to ensure their long term
availability and reliability.
2.3 Vibration-based fault diagnosis
Vibration analysis is the most important and most commonly used method for the
detection and diagnosis of faults in rotating machines. It is very widely used in the
industry to detect, locate and diagnose most of the common types of faults in one or
more rotating machine components [17]. Any abnormal vibrations discovered in the
rotating machinery gives a first indication that the rotating components of the machine
are failing. The use of vibration analysis for the diagnosis of machine operating
conditions can be carried out by comparing the vibration signals generated by a machine
operating in healthy conditions with those generated under faulty conditions. By
comparing these vibration signals, machine failure can be detected early, and thus the
possibility of significant damage occurring during machine operation can be reduced or
even eliminated using a condition monitoring system. The procedure of monitoring
using vibration analysis is dependent on various steps, including the application of
advanced signal processing programmes, so as to enhance the accuracy and reliability of
the faults diagnosis process. In this research study, vibration measurement techniques
are applied for the purpose of detection and diagnosis of blade faults in rotating
machines based on a simple setup, low cost equipment and quality analysis of the
results. Many studies have reported the importance of using vibration measurement for
fault detection and diagnosis in machines, as part of preventive and predictive
maintenance strategies [17-22]. In particular, Atoui et al. [19] described the use of
vibration signals for the detection and diagnosis of unbalances in rotating machines.
Other studies [23] also stated that the most established diagnostic technique for
monitoring the health of rolling elements bearings in rotating machinery is vibration
monitoring. The process of vibration measurement for predictive and preventive
maintenance programmes consists of two main parts [18]: (a) picking up raw vibration
data through the aid of vibration sensors such as accelerometers, proximity probes,
velocity meters, optical sensors, etc., (b) processing of the measured vibration signal
either in the time or frequency domains, using different advanced signal processing
tools developed using software such as MATLAB, LABVIEW, etc.. Figure 2.1 shows a
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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schematic representation of a turbo-generator, with several sensors installed for
vibration based health monitoring.
Vibration analysis has become an important CM technique, used to reduce or eliminate
approximately 80% of rotating components problems, especially rotor-related faults
such as misalignment and unbalance [24]. The cost of implementing an effective
preventive and predictive maintenance strategy is well under the cost of machine failure
(including damaged machine components, downtime, personnel injuries, etc.) [18].
Figure 2.1: A schematic of vibration condition monitoring transducers system on turbo-
generator set [20]
2.4 Blades Vibration
Rotating blades are considered the component most likely to cause failures in rotating
machinery. Turbine blades are exposed to various high dynamic loads during machine
operation, received directly from the steam jet and converted into driving forces. These
loads include thermal, inertia and bending loads [25]. Blade failure modes normally
occur as a result of cracking, high cycle fatigue (HCF), blade rubbing, blade root
looseness, erosion, creep and corrosion. Therefore, early fault detection is important in
reducing blade related failures and hence there is a need for a reliable and simple blade
health monitoring (BHM) technique. Al-Bedoor et al., [21] reviewed the different
methods that have been attempted for blade vibration measurements up to the year
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2002. In addition, Abdelrhman et al., [26] reviewed CM techniques used for the
detection and diagnosis of blade health conditions in rotating machines. In practice,
blade crack fault is not easily detectable, owing to the lack of specific crack detection
sensors, which in turn makes the application of indirect measurements such as vibration
analysis imperative. Blade health can be established by observing and comparing the
vibration levels at the blade natural frequency during normal and faulty operating
conditions [20]. Any significant changes in blade vibration amplitude or any shifts in
natural frequency should be investigated to prevent blade failure [20]. Blade vibration
can be classified based on two parameters, namely; the type of excitation force and the
way the excitation force affects the vibration [27]. Blade resonance vibration occurs
when the blade natural frequency matches an excitation frequency [27]. There are three
methods used for measuring blade vibration: (1) stationary testing, which measures
blades natural frequency in a static condition using instrumented impact hammer or a
loudspeaker, (2) rotational testing by measuring blades natural frequencies under the
influence of centrifugal forces, and (3) in-service testing by measuring blades natural
frequencies under operation conditions [28]. Typical steam turbine blades are shown in
Figure 2.2.
Figure 2.2: A turbo-generator: a steam turbine bladed system [29].
Shaft
Disc Blades Stages
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2.5 Blade failure modes
Rotating blades are considered the most common cause of failure in rotating machinery.
Blade failures normally occur as a result of cracking, high cycle stress, blade rubbing,
blade root looseness, foreign object damage (FOD), erosion, creep, corrosion, etc. It is
known that turbines contain numerous moving and stationary components, with many
of these components been subjected to very strenuous conditions inside the turbine. In
addition to that, out of the components inside the turbine, blades are the most likely
components to cause failure in rotating machines. Blades are exposed to high
temperatures; high pressures and stresses; and vibration resulting from aerodynamic
forces and centrifugal loads. All these factors can lead to failures in blades, and as a
result to machine breakdown. The most frequent blade failure causes in turbo-machines
include cracking, high cycle stress, blade rubbing, flutter, blade fouling, blade root
looseness, degradation from erosion, corrosion, deformation, etc. Meher-Homji et al.,
[6] and Carter [5] provided comprehensive reviews of blade failure modes in rotating
machines. Blade related failures are the most common faults in gas turbines observed
from operational experiences [5, 30]. Hee et al. [31] provided a review of blade fault
classification and also presented an overview of blade fault detection and assessment
techniques in gas turbines. Rao et al. [32] also presented a comparison of different
cumulative damage theories for blade life estimation. Leong [33] presented the common
types of blade faults and the vibration based methods used for their detection, using
three case studies on blade faults in gas turbines. In this section, the main blade failure
modes commonly encountered in rotating machines are presented with particular
emphasis on blade faults such as mistuned effects, blade root looseness, cracks on
blade(s), flutter, rubbing and fatigue.
2.5.1 Blades with mistuned effects
Mistuning constitutes small variations from one blade to another, which often occurs
due to differences between blades’ geometric properties; manufacturing and fitting
imperfection. The existence of such imperfections and irregularities are often inherent in
the manufacturing and assembly process of bladed discs, which is near impossible to
avoid. Choi et al., [34] and Petrov and Ewins [35] indicated that blade mistuned effects
lead to a significant increase in blades resonant response amplitude, and hence are often
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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referred to as a source of HCF in gas turbine engines. Ewins [36] considered mistuning
effects of blades on the natural frequencies of the assembled bladed disc, through the
conduction of several modal tests on different blades, so as to determine the effects of
mistuningeffectsontheblades’naturalfrequencies.
2.5.2 Blade root looseness
Blade root looseness fault in rotating machines occurs as a result of excess dimensional
tolerance (i.e. the gap) between the blade root attachment and the disc supporting
structure. The stress on a blade root increases when the tolerance gap increases during
blade rotation due to centrifugal forces, which may eventually cause cracks on the blade
root. In addition, if the blades are not correctly mounted and assembled on the disc, then
the blade root may become loose, which may result to rubbing during machine
operation lead to failure. Bhat et al., [37] observed that some variations occur in turbine
blades’natural frequenciesdue tomanufacturing tolerances.Leong and Hee [30] and
Lim and Leong [38] applied wavelet analysis for the detection and diagnosis of blade
root looseness during rotor coast-down. Figure 2.3 shows the case where the tolerance
gap between a blade root and disc is larger than normal; this case of blade root
looseness is a type of blade faults.
Figure 2.3: Blade failure of a gas turbine due to blade root looseness (adapted from Hee et al.,
2014) [31]
Tolerance gap larger
than normal
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2.5.3 Cracks on blades
Another very common cause of blade failure is crack, which can lead to catastrophic
failures in rotating machinery [39, 40]. Cracks on blades can be related to a variety of
factors including; foreign object damage (FOD), manufacturing flaws, high or low cycle
fatigue, resonant fatigue and stress corrosion. In a comprehensive review conducted by
Carter [5], it was reported that the most common cause of cracks in gas turbine blades
results from torsional forces or FOD. In a survey conducted by Rao and Dutta [41], it
was highlighted that approximately 50% of turbine blades failures can be attributed to
fatigue, stress corrosion, crack and corrosion fatigue, while 40% of blade failure causes
are yet to be fully understood. Figure 2.4 shows a typical crack on steam turbine blade.
Figure 2.4: A typical crack on a blade in a steam turbine (adapted from Hahn and Sinha, 2015)
[42]
Crack
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2.5.4 Blade flutter
Flutter phenomenon can be defined as self-excited and self-sustained vibration of
rotatingmachines’ blades, owing to the interactions between aerodynamic forces and
blade vibration displacements. However, the enhancement of blade designs through
rigorous theoretical simulation (including finite element modelling and computational
fluid dynamics) of blade performance under different conditions may help minimise or
eliminate the flutteringof rotatingmachines’blades.Vedeneev et al. [43] described a
numerical method for identifying the emergence of flutter in compressor blades. Mazur
et al. [44] also investigated the failure of several blades at the last stage of a low
pressure (LP) turbine of two 660 MW units.
2.5.5 Blade rubbing
Blade rubbing is another significant cause of faults in turbo-machinery [45]. Blade
rubbing faults in gas turbines represent approximately 23% of total blade faults [46].
Blade tip rubbing occurs as a result of contact between the stationary (casing) and
rotating (blades) parts of the turbine, leading to blades tip wear or failure. The primary
reason for blade-to-casing rubbing is high lateral rotor vibration and displacement of the
rotor centre line due to rotor misalignment. In aero-engines for instance, there are many
other reasons for blade rubbing including the deformation of engine casing due to heavy
landing, leading to contact between the compressor blade tips and the casing. This can
also happen as a result of blade expansion due to high temperatures and tight clearances
between the blades and the casing, which is common in modern-day aircrafts as the gap
between blade tips and the engine casing is reduced. Many researchers investigated
blade rubbing faults [47-49], while others investigated blade rubbing detection in
rotating machines using vibration techniques [30,50, 51]. For example, Mba and Hall
[52] applied acoustic emissions for detecting rubbing in rotating machines. Figure 2.5
shows the picture of a failed rotating machine blade due to rubbing.
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 2.5: Blade failure in a gas turbine due to rubbing (adapted from Hee et al., 2014) [31]
2.5.6 Blade fatigue
The operation of rotating machines such as steam turbines under high temperature,
pressure and speed contributes significantly to blade fatigue failure, especially HCF
[53]. Blade fatigue faults are reported to contribute as much as 18.5% of all blade
failures in gas turbines [46]. Blade fatigue failures are often due to irregularities in
mechanical behaviour and manufacturing defects [54]. Blade fatigue failures can be
broadly classified into two groups;
(a) High cycle fatigue (HCF): This occurs infrequently in turbine blades unless when
initiated by other damage sources such as FOD or the occurrence of abnormal vibrations
in the engine, causing extensive bending of the blades [5]. Else, HCF can be caused by
aerodynamic excitation or blade self-exited vibration and flutter [55].
(b) low cycle fatigue (LCF): This is related to frequent start-up and shut-down operation
of the machine under high centrifugal stress imposed by high speed and thermal stress,
which eventually causes cracks to occur at several locations on the blades and bolt holes
or bores of the bladed discs [26]. Figures 2.6 and 2.7 show the distribution of failure
modes including HCF, LCF and other failures associated with different engine parts.
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 2.6: Failure mode distribution in jet engines (adapted from Cowles, 1989) [56]
Figure 2.7: Distribution of HCF on jet engine components (adapted from Cowles, 1989) [56]
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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In this research study, the chosen three types of blade failure modes are healthy with
mistuned effect, blade root looseness and crack(s) in blade(s) for the experimental tests.
This three failure modes are common in rotating machines according to the Figure 2.6.
From the pie chart percentages, failure modes that commonly cause cracks in the
rotating blades leading to blade failure are: HCF 24%, LCF 12%, corrosion 8%,
mechanical damage 12%, overstress 12%, and other types of fatigue 13%, which means
that a total of 71% of blade failure modes can result in crack in blades. Blade root
looseness occurs as a result of materials 7%, the manufacturing process 11%, thermal
stress 1%, and overstress 12%. Blade mistuning occurs as a result of materials 7%
and/or the manufacturing process 11%. By choosing these three common blade faults,
this research study will cover the most common types of blade failure modes in rotating
machines.
2.6 Blade vibration measurements
Although studies [57], have shown that blade vibration is a significant contributor to
most major and costly failures in rotating machine, especially considering the
challenges associated with acquiring measurements that will explain the condition of
bladed rotating machines during operation. There are many direct measurement
techniques for monitoring blade conditions in situ, including the use of strain gauges
attached directly to the blade; laser Doppler systems and optical methods. On the other
hand, indirect techniques such as vibration measurements involve picking up vibration
signals from the casing, bearing pedestals or the main rotor and then conducting
diagnosis through the application of techniques such as instantaneous angular speed
(IAS). Al-Bedoor [58] and Abdelrhman et al., [26] respectively provided extensive
reviews on the different approaches for measuring blade vibrations, as well as the
strengths of each technique.
2.6.1 Strain gauge
Strain gauge is a direct measurement tool and a conventional method widely used in
blade health monitoring for estimating the blade vibration properties. During blade
health monitoring, the strain gauge is often attached to one or more blades, so as to
measure blade deflection. The use of strain gauges offers the least intrusive amongst
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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direct measurement techniques. A typical strain gauge is shown in Figure 2.8. The
application of strain gauges for blade health monitoring has also been described by
several studies, for instance, Russhard [59] provided a review of the development of
strain gauges in monitoring blade vibration over four decades, while Knappett and
Garcia [60] described the measurement of blade strain during machine operation, which
can be eventually used for estimating the stress and possibly the prediction of residual
stress [61]. In a different study, the optimal placements of strain gauges for measuring
rotating blade vibration were investigated by Szwedowicz et al. [62]. Over the years, the
optimal method for collecting data was through a combination of strain gauges with
radio telemetry and slip rings [59]. The main strength of this method involves the ability
to measure blade vibration by installing strain gauges on individual blade surfaces as
well as on the bladed disc as shown in Figure 2.9 (a) and (b), which allows facilitates
the measurement of vibrations on individual blades and the bladed disc. Another merit
of this method is the ability to directly record continuous and high response data.
However, this method is also associated with some disadvantages, including the
mounting of the sensors on the external surfaces of the blade (i.e. the flow path), which
in turn exposes the sensors to harsh operating conditions (such as high temperatures and
centrifugal forces) that contribute to their short life span [58]. Additionally, it is difficult
to access and transmit the generated signals from strain gauges to the external
processing device, which is the reason for the incorporation of complex systems of slip
rings or radio telemetry [63], which eventually increase the overall cost blade CM [64].
The complex shapes of the blades sometimes make it difficult to locate suitable
mounting surfaces for the strain gauges.
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 2.8: Foil strain gauges: (a) schematic of foil gauge (reproduced from Russhard, 2015)
[59] (b) sensor attached on the object [65]
Figure 2.9: Typical mounting of strain gauges: (a) on bladed discs, (b) on blades (a and b
reproduced from Russhard, 2015) [59], and (c) on a steam turbine blade (Reproduced from
Hahn and Sinha, 2013) [66]
(a)
(c)
(b)
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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2.6.2 Blade tip timing (BTT)
Blade tip timing is a direct and non-contact measurement method. Blade tip sensors
(capacitance, optics, and eddy current) are commonly used for monitoring blades
parameters in steam and gas turbine engines [67], as well as for diagnosis and prediction
of blade conditions in rotating machines during operation. Some of the most commonly
used BTT sensing techniques for blade vibration monitoring include capacitive,
inductive, optical, microwave, infrared, eddy-current, pressure and acoustic based
sensors [68]. Many studies [53, 69-72] have investigated the use of BTT as a vibration
based fault detection in rotating blade assemblies, and it was observed to be a useful and
effective method for blade health monitoring by measuring blade parameters such as
natural frequency, time of arrival and angle of arrival, hence providing rich information
that enhance the detection of a variety of blade faults including blade rubbing, cracks,
bending and blade looseness. BTT works by using a number of capacitance or optical
probes mounted on the enclosure of each engine blade stage as shown in Figure 2.10,
and the minimal number of probes is one sensor per stage. These probes measure the
time when blades tip passes a probe. Figure 2.11 shows a typical BTT measurement
system consisting of four proximity probes and a shaft speed tachometer for measuring
engine driving shaft speed. The time of arrival (TOA) of the blade is compared to that
when no dynamic motion is applied. BTT has received considerable attention in recent
years as it is capable of identifying blades that exhibit high vibration [60, 73] and
therefore BTT is considered a promising approach for blade vibration monitoring [74,
75]. Using non-contact BTT systems makes it possible to measure blade(s) vibration
and to detect blade health conditions in turbo-machinery [75]. In general, capacitance or
optical probes have been used extensively for these reasons [64].
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 2.10: Schematic of a two-coil inductive sensor for blade tip clearance measurement
(Reproduced from Li et al., 2014) [76]
Figure 2.11: Non-contact blade vibration tip timing measurement system (Reproduced from
Tamura et al., 2014) [77]
BTT is suitable for early detection of blade rubbing in gas turbines which has already
provided good results [26]. However, BTT systems suffer from some drawbacks,
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Owing to the fact that optical probes require frequent maintenance to keep the lenses
clean from fouling [64]. The weight of system unlikely to add to the weight of aero-
engines which is fitted on an operational engine, which in airplanes need to reduces the
weight BTT systems are also sensitive to resonance vibration [78], intrusive and can be
expensive methods. In addition, the measurement system is limited to the blade tip,
thereby allowing for measurements only when the blade tip passes the probes. However,
the probes are sometimes unable to detect deflection responses that do not provide
sufficient tip motion. Another disadvantage is that incorrect probe spacing and if the
number of probes not enough can lead to vibration frequency aliasing [79]. However,
the recent initiatives on the combination of stain gauges and BTT probes in a single
system offers the potentials to overcome some disadvantages and hence achieve better
results of blade monitoring. However, both BTT and strain measurement methods are
intrusive and exorbitant. Hence, there is a need for more reliable and simple but robust
methods to meet the requirements of BHM. Figure 2.12 shows a typical BTT system on
the casing of a last stage steam turbine.
Figure 2.12: Typical mounting of BTT probes system on a turbine casing for BHM of last stage
blades (Hahn and Sinha, 2013) [66]
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2.6.3 Torsional vibration
Torsional vibration in rotating machines is defined as the oscillatory movement of a
rotor as it rotates, or an oscillatory angular motion causing twisting in the rotating shaft
[80]. Torsional vibration can also be viewed as the cyclic variation of shaft speed.
Torsional vibration in rotating machines can cause various failures such as gear-tooth
breakage, blade faults due to blade fatigue in steam turbines [81], and shaft cracks. Al-
Bedoor [82] and Yang and Huang [83, 84] investigated the coupling between shaft
torsion and blade bending vibrations using a theoretical model of shaft-disk-blades
assembly. The results showed that a blade vibration signature can be extracted from the
shaft torsional vibration. Also, Huang and Ho [85] and Al-Bedoor [86] described the
coupling between blade bending and shaft torsion vibration for shaft-disc-blades
systems. Due to the interactions of vibration signals between blade bending and shaft
torsional vibration, this characteristic can be used to investigate the changes in blade
frequency, and hence detect blade faults. Therefore, shaft torsional vibration can be
used as a diagnostic tool to detect rapid changes in blade natural frequencies coupled to
shaft torsional modes, which enhance blade faults diagnosis as shown in Figure 2.13 (a)
and (b). Rotating machine vibration signature can be extracted from measurements of
shaft torsional vibration and then used for diagnosing machine conditions. This
technique can be used to monitor the changes in shaft torsional vibration frequencies
which are associated with blades frequencies, allowing for the assessment of blade
crack propagation [87]. There is limited research conducted on investigating the ability
of shaft torsional vibration measurements to provide information on blade vibration.
However, there is a potential for a new approach of indirect measurement of blade
vibration by measuring rotor torsional vibration [57, 88]. Non-intrusive measurement of
shaft torsional vibration signals in rotating machines has been a subject of interest in
research [89-91]. Experiments showed that shaft torsional vibration data analysis can be
effective for the detection and diagnosis of blade cracks, based on the changes in blades
natural frequency [89, 90, 92, 93] as shown in Figure 2.13. The changes in blades
frequency can be identified as seen in Figure 2.13 (b) for faulty blades compared to
profiles of healthy blades shown in Figure 2.13 (a).
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 2.13: Frequency spectrum of measured torsional vibration signal: (a) healthy tuned
blades, (b) one faulty blade (mistuned) (adapted from Maynard et al., 2000) [94]
Maynard and Trethewey [91] conducted simple experimental tests to identify blade
cracks in gas turbines through the application of the shaft torsional vibration method.
Other studies by Al-Bedoor [95] and Maynard and Trethewey [96] also reported on
experimental tests conducted to extract blade vibration data from shaft torsional
(a)
(b)
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
51
vibration measurements. The results indicated that the blades’ frequency responses are
often reflected in the shaft torsional vibration signals. Another study [97] on blades and
shaft vibration monitoring involved the application of random vibration excitation on
blades to determine the natural frequencies and mode shapes, through finite element
(FE) modelling. The blades and shaft torsional vibrations were monitored through the
combination of strain gauges installed on the blades and shaft, as well as on-bearing
accelerometers. The results showed that blade vibrations were apparent in the shaft
torsional vibrations. Maynard and Trethewey [91] have also demonstrated the field
application of the use of torsional vibration for tracking cracks in the shaft and blades of
rotating machines. Analytical simulations [82, 85, 98] have also shown the potential of
shaft torsional vibration in CM of turbine blades.
2.6.4 On-bearing vibration (OBV)
On-bearing vibration measurement is an indirect technique used to capture vibration
signals from the bearing pedestals of rotating machines and then the signals are
analysed to identify the dynamic behaviour of the blade(s), which provides information
about the blade health during machine operation. OBV measurements are simple to
initiate, non-intrusive and non-expensive method used to investigate blade health
conditions. Vibration signals extracted obtained through OBV measurements contain
information of machine vibration responses, including blades responses, which makes it
a valuable method for understanding machine condition [66, 99]. Hahn and Sinha [42]
carried out vibration measurements on a steam turbine generator (TG) using
accelerometers installed on bearing pedestals. It is suggested that using OBV measured
data of transient machine operation (run-up and run-down) during normal conditions is
important to identify and diagnose machine faults [20]. Sinha et al., [100] recently
analysed the in-situ measured vibration data at the bearing pedestals during the steady
state and transient operation of steam turbines, so as to understand machine dynamics as
well as identifying the root causes of failure of the last stage blades of the LP turbines.
2.6.5 On-casing vibration (OCV)
On-casing vibration is also an indirect measurement method used to diagnose blade
health conditions. The method is based on measuring casing vibrations by extracting the
vibration response signals of rotating blades using accelerometers mounted on the
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machine casing (i.e. at each rotating blade stage on the outside surface of the casing)
during machine operation. The pressure of air or steam generated from the flow as well
as themovementof rotatingblade tipsoftenaffect the innersurfaceof themachine’s
casing, and this gives valuable information on the blade vibration behaviour. Hence,
comparing the dynamic behaviour of blade(s) for varying cases of blade health
conditions through the analysis of machine on-casing vibration data can be useful in
BHM. Mathioudakis et al. [101] investigated the relation between casing vibration
responses of industrial gas turbines and engine operating conditions. Rao and Dutta
[102, 103] also investigated the use of casing vibrations to diagnose and detect blade
vibrations by monitoring the changes of blade passing frequency (BPF) components.
BPF vibration amplitude was found to be extremely sensitive to blade conditions during
operation [102]. An experimental work was carried out to estimate blade natural
frequencies from casing vibrations using proximity probes, so as to detect blade health
condition [104]. In addition, analytical and FE model has been developed to detect
blade frequencies from on-casing vibration response [105]. A combination of an
experimental work with an analytical model was recently presented to estimate blade
natural frequencies from on-casing vibrations as well as to describe how the internal
pressure of a turbine affects the blades frequency [106, 107].
2.7 Signal processing techniques
The most common signal processing techniques used in this research study for analysis
of blade vibration data in order to diagnose and detect blade fault conditions are
described in the following sections with literature related to these techniques.
2.7.1 Time domain analysis
Time domain analysis is a simple method for analysing vibration signals, and it is
widely used in CM of machine parts conditions. The time domain is a plot of the
relation between signal amplitude and time. Depending on the type of the vibration
sensor, the vibration signal used in time domain analysis can be acceleration, velocity or
proximity. Time domain data can be used in the identification of rotor faults in rotating
machines and several studies reported reliable identification of such faults. Goldman
and Muszynska [108] and Lee and Han [109] presented effective rotor fault
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
53
identification methods that use orbit shape analysis. Moreover, Doguer and Strackeljian
[110] recently presented a new method that uses time domain data to identify bearing
faults in rotating machines.
2.7.2 Frequency domain analysis
Frequency domain analysis (e.g. fast Fourier transform, FFT) is a mathematical method
which converts signal data from time domain into frequency domain. Frequency domain
analysis is the most commonly used process for analysing vibration signals in rotating
equipment, and it represents a very useful method particularly during steady state
operation when the signal is stationary (i.e. frequency components not variable over
time). The main disadvantage of frequency domain analysis is featuring only frequency
information of signals and losing any time information as well as capturing only two-
dimensional information [111].
2.7.3 Short time Fourier transform (STFT)
Short time Fourier transform (STFT) analysis is a mothed capable of providing time and
frequency information when analysing vibration data in the same plot. It is applied by
dividing the time series signals into smaller segments (windows), then applying FFT to
all sections individually, followed by reassembling the data into a 2D function of time
and frequency, offering compromised information about both. The STFT plot normally
shows time, frequency and amplitude information in the form of either waterfall plots or
contour maps [112].
2.7.4 Instantaneous angular speed (IAS)
Shaft instantaneous angular speed (IAS) signals extracted from rotary encoder raw
signals are representative of shaft torsional vibrations. This method can be employed for
calculating the time interval changes between the successive pulses of the encoder. This
method is generally non-intrusive and non-exorbitant and is currently the focus of
research for different applications on various types of machines. Li et al. [113]
published a review of the methods employed for the measurement of IAS. IAS based
methods can be applied in fault diagnosis in rotating machines [114]. Examples of this
application include using torsional vibrations (IAS) for the identification of faulty
CHAPTER 2 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
54
combustion cylinder(s) in diesel engines [115-119] by monitoring the crank shaft IAS
signals. Another example describes using IAS signals to detect gearbox defects [120].
The application of IAS has also triggered the understanding that transverse blade
vibration is often reflected in the shaft torsional vibration, owing to the coupling
between shaft torsional and blade bending vibrations. It was experimentally verified on
small rigs, and the mistuning effect on the blade natural frequency was also observed
[89, 90].
2.7.5 Order tracking analysis (OTA)
Order tracking techniques represent a mothed based upon FFT. The method requires a
time domain data with tachometer signals to accurately compute the frequency of the
order at every time point. This is carried out by re-sampling the vibration data at times
equal to phase increments, providing a constant angular frequency. This equal phase
spacing used to re-sample the signals is determined synchronously with reference to
tachometer signals. Using OTA allows the sampling rate of data to be synchronized
with the shaft rotation speed to confirm the sampling locations as constant angles of the
shaft. Therefore, even if the machinery speed changes during run-up or coast-down, the
sampling will still happen at the same shaft angle and the frequency component will be
presented as a function of the order of shaft speed. FFT is subsequently applied to the
data to transform into order domains presented as sine waves of the amplitude of the
order (spectrum order). The order-tracking technique has recently become an important
approach for diagnosing faults in rotating machinery [121].
2.7.6 Time synchronising averaging (TSA)
Time synchronous averaging (TSA) is a time domain analysis method used to remove
any signal components not related to shaft rotation rate, which can reduce the noise in
spectra of complex signals. TSA works by calculating spectra averaging of the data after
performing FFT on each signal sample. This method is done with respect to tachometer
signals obtained from shaft rotation speed. The main advantage of this method is the
removal of all non-synchronous parts of the signal and any random noise from the shaft,
blades and gears with a rotational period not matching the averaging period [122]. In
this study, TSA is used to exclude all vibration components from signals except the
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events related to the shaft and blades, which can then be used for monitoring any
changes in the blade frequency region, thus detecting blade(s) faults.
2.8 Research plan to investigate blades’ health using alternative vibration
measurements
For monitoring the blade health in real time, it is very useful to obtain on-line blade
vibration information. Researchers are always looking for a reliable method with few
disadvantages to accomplish this target. However, from the previous studies, it was
shown that both BTT and strain measurement methods are intrusive and exorbitant
methods in addition to other disadvantages discussed in Sections 2.6.1 and 2.6.2. In
view of these complications, the need to explore more reliable, non-exorbitant, non-
contact, non-intrusive and simple but robust methods that will effectively satisfy the
requirements of BHM becomes imminent. Moreover, it may be advantageous to select a
method based on shaft torsional vibration using IAS encoder signals as an alternative to
previous methods discussed in the sections mentioned above. Other chosen methods are
the OBV and OCV measurements which are simple and non-exorbitant to use.
However, a comparison of the three methods is required in order to select which of
these methods is more reliable for BHM.
2.9 Summary
According to the literature, rotating blades are considered one of the most common
causes of failures in rotating machines such as turbo-generators. Blades are exposed to
high mechanical loading and stress due to the extreme changes in both temperature and
pressure. Therefore, the need for a reliable early fault detection tool is important to
reduce blade related failures, and hence increase operational safety, efficiency and
reliability. In this chapter, a comprehensive literature review is presented, summarising
the previous research works on diagnosis and detection of blade vibration health
conditions in rotating machinery. Firstly, the chapter provided a brief description of CM
approaches which are commonly used for faults diagnosis in rotating machines. Then, a
brief discussion on vibration-based fault diagnosis in rotating machines was provided.
An overview of earlier studies on blade vibration condition monitoring is provided with
some blade failure modes. Finally, various vibration measurements which are
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commonly used to detect and diagnose blade health conditions in rotating machines are
presented, along with their advantages and disadvantages, in addition to the signal
processing techniques are used in this study. In summary, to identify which of these
methods is more effective for the purposes of this study with regards to the advantages
and disadvantages of measurements based on strain gauges and BTT, the last three
measurements, i.e. shaft torsional vibration based-on IAS, OBV and OCV, were
eventually selected.
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CHAPTER 3
RIG DESIGN AND EXPERIMENTAL SETUP
3.1 Introduction
In this chapter, details of design and manufacture of the test rig are discussed. The rig is
designed to simulate rotating machine blades in order to detect blade faults due to blade
vibration problems. The test rig was designed to allow measurements of the torsional
shaft vibrations, on-bearing vibration (OBV) and on-casing vibration (OCV) for the
purpose of detecting blade health condition.
A detailed description of the design, including the full dimensions of the shaft-disc-
blades system, is given, based on the results of the modelling done by means of the
Finite Element model (FEM) using ABAQUS software [123]. The rig was
manufactured using the dimensions obtained in accordance with the FEM. The
experimental work for this research was conducted on the rotating shaft-disk-blade
assembly, which is located in the Dynamics Lab in the School of Mechanical,
Aerospace and Civil Engineering (MACE) at the University of Manchester. A full
description of all rig components, equipment and measurement devices used in the
construction of the test rig is also provided.
3.2 Finite element modelling
The modal analysis was carried out based on a FEM using an ABAQUS 6.10 software
package to simulate the shaft-disc-blades system in order to estimate the natural
frequencies and the mode shapes, with the aim of obtaining optimum dimensions for the
rig design from a suitable natural frequency for blades, which is obtained using a FEM.
With the created model, some random dimensions for the rotating parts of the shaft-
disc-blades system of the rig were chosen initially and then these dimensions were
modified until appropriate results of natural frequency with reasonable dimensions were
obtained. These optimised dimensions were used for the final design.
For the simulation of the shaft-disc-blades system using ABAQUS 6.10, the Element
C3D8R (8-node solid brick) was chosen for model analysis, which is suitable for
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dynamic analysis of the solid parts as it is compatible with the shaft-disc-blades system.
For linear incompatible modes, the ABAQUS explicit module was used with a 3D
eight-node linear brick element with reduced integration and hourglass control (C3D8R)
for modelling the shaft-disc-blades [123].
3.3 Primary design model
In order to obtain optimum dimensions for the test rig design, random dimensions were
chosen for the shaft-disk-blades system as listed in Table 3.1, in which the simulated
blades were chosen to be cylindrical rods. The system was created using a FEM using
the ABAQUS package.
Table 3-1: Shaft-disk-blades model dimensions
Part Property Value
Shaft Material Steel (E = 210 GPa, = 7800 kg/m3)
Length 1000 mm
Diameter 20 mm
Disk Material Steel (E = 210 GPa, = 7800 kg/m3)
Width 20 mm
Outer Diameter Do 50 mm
Inner Diameter Di 20 mm
Blade X 8 Material Steel (E = 210 GPa, = 7800 kg/m3)
Length 120 mm
Diameter 10 mm
(E),Young’smodulus;(), density; E and are theoretical values
A view of a typical modelling mesh for the shaft-disc-blades system is shown in Figure
3.1.
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(a)
(b)
Figure 3.1: Typical FE mesh for the shaft-disc-blades model: (a) side view, (b) front view
The simulation and optimisation of the shaft-disk-blades system is done by discretising
the body into sub-regions which are called elements. Each element in the model
represents a discrete portion of the physical structure, which is, in return, represented by
many interconnected elements; the type of element used in this model is C3D8R. The
support condition of the shaft was assigned using the BOUNDARY option available in
ABAQUS (SIMULIA, 2010d) by constraining and releasing the corresponding degrees
of freedom at each end of the shaft. In the present numerical model, fifty frequency
modes were computed which conforms to the experimental results.
The natural frequencies for shaft and blades were obtained. However, according to the
results obtained from the pre-design model of the system, the natural frequency value of
the blade bending was extremely high at 699.12 Hz, and the shaft bending natural
frequency value was particularly low at 33.894 Hz. On the other hand, real values of
lowpressureturbineblades’frequencyinsteamturbinesrangebetween100and120Hz
[124]. For this reason, the model was modified to obtain more logical frequency values
for the blades and shaft close to the values observed for steam turbines in order to
achieve optimum design of the rig. The system design was modified by trying the
dimensions listed in Table 3.2, which are chosen related to shaft and blades natural
frequency from FEM.
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Table 3-2: Model properties and dimensions of the shaft-disc-blades system including design of
blade tip mass
Part Property Value
Shaft Material Steel ( E=210 GPa, =7800 kg/m3)
Length 600 mm
Diameter 20 mm
Disk Material Steel ( E=210 GPa, =7800 kg/m3)
Width 20 mm
Outer Diameter Do 50 mm
Inner Diameter Di 20 mm
Blade X 8 Material Steel ( E=210 GPa, =7800 kg/m3)
Length 110 mm
Diameter 5 mm
Blade tip mass X 8 Material Steel ( E=210 GPa, =7800 kg/m3)
Width 10 mm
Outer Diameter Do 20 mm
Inner Diameter Di 5 mm
Mass 25 grams
(E),Young’smodulus;(), density; E and are theoretical values
The finite element (FE) optimisation model of the shaft-disk-blades system with blades
tip masses was simulated as shown in Figure 3.2, using ABAQUS 6.10.
Figure 3.2: Typical FE mesh for shaft-disc-blades with blade tip mass model: (a) side-view
with bearings location, (b) front view
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The boundary conditions were applied on the model by assuming the bearing position
on theshaftatdifferentdistanceswithin65mmand55mmfromtheshaft’sends,as
shown in Figure 3.2, which considers the system simply as a support beam; that is, with
no movement in two directions (X,Y).
According to the results obtained from the FEM, the natural frequency value of blade
bending decreased sharply to 92.911 Hz compared to the pre-design model, and the
shaft natural frequency also increased sharply to 129.57 Hz compared to the pre-design
model frequency value. The new natural frequency values for blade and shaft bending
were within reasonable parameters and they confirmed that the dimensions and
materials made an optimum design for the rotating parts in rig fabrication and
manufacture. Figure 3.3 shows cylindrical blades and Figure 3.4 shows a photograph of
a rig with cylindrical blades.
Figure 3.3: Cylindrical blades: (a) blade with blade tip mass, (b) blade without blade tip mass,
(c) crack on blade 20% of blade diameter
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Figure 3.4: Experimental rig with cylindrical blades
Note that for more self-excitation force to the blades during machine running, a mass is
added on the tip of each blade, as shown in Figures 3.3 and 3.4. More than one mass can
be added at the end of each blade. The main reason for choosing cylindrical blades in
the initial design rather than rectangular ones, which are more similar to real blades, is
that cylindrical blades can easily simulate mistuned blade(s) faults. The blades were
tuned to a specific frequency by adjusting the tip masses on in each blade. Cylindrical
blades were also used by Maynard and Trethewey [90-93, 96] in many research studies
(Figure 3.5) to simulate blade faults and for easy diagnosis of blade health conditions.
Figure 3.5: Blade fault diagnosis using cylindrical blades: (a) on test rig, (b) bladed disc [90]
Blade tip mass
(a) (b)
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3.4 Final optimised model
According to the experimental results obtained from the test rig, which was
manufactured according to an acceptable design of the FEM, cylindrical blades with or
without tip masses were not sufficiently excited to detect blade faults during machine
running. On the other hand, the results seem to suggest no changes in the blade(s)
response for both healthy and faulty conditions of the blades. Moreover, five strong
magnets, type E1020, were added opposite one side of the blades, as shown in
Figure 3.6, to increase stimulation of excitation force on the blades during machine
operation (run-up or run-down). Also here, when using the magnets, there were no
changes in blade response for both healthy and faulty cases.
Figure 3.6: Five magnets with an attached ring opposite the cylindrical blades
8-Cylinderical
blades
5-Strong magnets type E1020
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Therefore, according to the results, it was necessary to change the design from
cylindrical blades to rectangular blades that were flat and more similar to real turbine
blades, but with the same dimensions which were obtained using the FEM. The blades
were manufactured as flat plates with two sizes of blades (long and short) for different
types of experiments. Blade properties and dimensions are listed in Table 3.3.
Table 3-3: Shaft–disc–blade(s) data for long and short blades
(E),Young’smodulus;(), density; E and are theoretical values
The FE model was carried out to determine the blade(s) natural frequencies, which were
found to be within an acceptable range for the design. The model of a shaft-disc-blades
system for long and short blades was created using 8-noded rectangular solid elements
of type C3D8R. According to FE results obtained from the model, the natural
frequencies for the 8 long blade(s) bending were between 112.07 and 122.06 Hz due to
the mistuned effect, and those for the 8 short blades were between 184.50 and 201.64
Hz, as shown for the first mode of long and short blades in Figures 3.7 and 3.8. The
shaft frequency mode value determined was 141.00 Hz. These values of natural
Part Property Value
Shaft Material Steel ( E=210 GPa, =7800 kg/m3)
Length 600 mm
Diameter 20 mm
Disc Material Steel ( E=210 GPa, =7800 kg/m3)
Width 20 mm
Outer Diameter Do 50 mm
Inner Diameter Di 20 mm
Long Blade(s) (8 numbers) Material Steel ( E=210 GPa, =7800 kg/m3)
Length 110 mm
Width 20 mm
Thickness 2 mm
Short Blade(s) (8 numbers) Material Steel ( E=210 GPa, =7800 kg/m3)
Length 73 mm
Width 25 mm
Thickness 2 mm
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frequency for long and short blades and shaft bending are within an acceptable range,
and they confirmed that the dimensions of the model formed an optimum design for rig
manufacturing.
Figure 3.7: FE model of long blades’ first natural frequency with matched mode shape
Figure 3.8: FEM of short blades’ first natural frequency with matched mode shape
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3.5 Manufacturing of the experimental rig
For this research study, a rig was manufactured using dimensions in accordance with
the FEM, carried out using ABAQUS software. The rig was designed to simulate
rotating machine blades in steam turbines in order to detect blade faults due to vibration
problems arising from mistuned blades, blade root looseness and cracks in blade(s). The
rig was designed to allow measurements of the shaft torsion vibration, OBV and OCV
in order to investigate blade dynamic behaviour during machine operation of run-up or
run-down for blade health monitoring (BHM). The experimental work of this research
was conducted on the rig of the rotating part of shaft-disc-blade, which is located in the
Dynamics Lab at the school of MACE at the University of Manchester. A photograph of
the test rig including the measurement systems is shown in Figure 3.9.
Figure 3.9: Photograph of the experimental rig with rectangular blades
3.5.1 The experimental rig
In order to investigate the feasibility of torsional vibration, OBV and OCV
measurement techniques for incipient blade deterioration in rotating machinery, a small
experimental test rig was built in the Dynamics Lab at the school of MACE at the
University of Manchester for the purpose of this study. The rig was mainly designed to
study blade behaviour due to different fault conditions, such as: healthy with blade
mistuned effects, blade root looseness and cracks in blade(s), using the three
measurement techniques of shaft torsional vibration, OBV and OCV.
The test rig mainly consisted of the following parts, as shown in Figure 3.9:
Accelerometer
8- Rectangular Blades Tacho Sensor
Flexible Coupling Driver Motor
Bearing Encoder
Disc Shaft
Casing
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(a) A driver 3-Phase motor of 1 HP power.
(b) 2 SKF ball bearings, type YS 20 TF.
(c) A steel shaft of 600 mm length and 20 mm diameter, holding one or more bladed
discs.
(d) A variable speed controller connected to the motor and PC for controls: motor
steady state, run-up or run-down speed.
(e) A flexible coupling between the motor shaft and rig rotor.
3.5.2 Parts of the experimental rig
The rig contained the following main parts, which are discussed in this section.
3.5.2.1 Motor
The motor used on the rig was manufactured by Beatson Fans & Motors Ltd. A
photograph of the motor, which drives the main rotor of the test rig, is shown in
Figure 3.10, and the specifications are listed in Table 3.4. The motor was connected to a
speed control unit for control of motor shaft rotating speed. The motor base was
integrated into a horizontal plate by means of four attachments, which consisted of a
hollow cylindrical support linked by four bolts passing through it and fixing it to the
rigid horizontal plate. The horizontal plate was attached to the foundation table by
means of four bolts.
Table 3-4: Specification of the motor
1 Maker Beatson Fans & Motors
2 Type Imperial B56 Frame
3 Power 720W/1 HP
4 Maximum Speed 2800 rpm
5 Poles 4, 1500 revs.
6 Quality High
7 Body Drip-Proof
8 Power supply 240V/415V AC Three Phase
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Figure 3.10: Electrical motor
Every effort was made to align the motor shaft centre with that of the rotor centre by
using a dial gauge in order to prevent misalignment. In this case, a rim-face method was
used to align the motor shaft with the main shaft of the rig. Two dial indicators were
employed to find the relative position of the movable shaft of the rig with respect to the
stationary shaft of the motor. The following the steps were carried out: (1) removal of
all dirt and dust from the bottom of bearings and motor bases, (2) setting the bases in
their places not fully tightened, (3) using the dial gauges on the rim to set the two shafts
at the same level horizontally, (4) rotating the dial gauge around the rim to make sure
the two shafts were in line, and (5) using the dial gauge on the face of the shafts to make
sure the two faces were vertically aligned. When misalignment existed, shims were used
under the bearing bases until the gauge reading indicated alignment. Finally, the bolts
on the bearings were tightens and the gauges were removed.
3.5.2.2 Shaft
The main rig rotor was a solid steel shaft with a circular cross section of 20 mm
diameter and a length of 600 mm. The shaft was manufactured by SKF Company. The
steel shaft hardness was 60 HRC and mass was 1.47 kg.
3.5.2.3 Bladed disc
The blades and disk were of steel and were designed to simulate real blades in rotating
machines. The disc could be adjusted anywhere between the two bearings as required.
The disk had a central hole. The outer diameter, inner diameter, thickness and mass are
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given as 50 mm, 20 mm, 20 mm and 1.176 kg, respectively. The disc was designed to
hold eight blades placed on the outer surrounding at an equal angle of 45 degrees
between each blade. The disc was designed with eight threaded holes at the mid-point of
the thickness of the outer surrounding of the disc, to install the blades. The disk was
fixed by 2 screws to the rotor shaft through two threaded radial holes of a diameter of 5
mm, and there were also 8 threaded holes, 4 holes on each side, for small screws to
tighten the blades’ roots and prevent any movement of the blades during rotation, as
shown in Figure 3.11 (a) and (b). The blades were manufactured in steel as rectangular
plates with dimensions as listed in Table 3.3. They were designed with threaded ends;
one to attach the blades to the disc and the other end being welded to the middle of the
flat plate. Two types of blades (long and short) were designed and manufactured in
order to conduct the experimental tests. A photograph of the blades is shown in
Figure 3.12.
Figure 3.11: Bladed discs: (a) Long blades, (b) Short blades
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Figure 3.12: Photograph of a long blade and a short blade
3.5.2.4 Ball bearing
There were two identical pedestal ball bearings used in the rig, which were SKF type
YS 20 TF as shown in Figure 3.13. Along with the bearing, cast housings and grub
screw locking were used.
Figure 3.13: Pedestal ball bearing unit
The bearing cast housing was fixed into a vertical rigid steel block by two bolts for each
bearing. The bearing was attached to the vertical plate by means of two threaded bolts
and the vertical plate itself was bolted to the base plate which was attached to the
horizontal plate along the rig. The bearings foundation was aligned along the rig plate
towards the motor foundation in such a way that the centre of the bearings’ holes was in
line with the motor shaft centre.
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3.5.2.5 Flexible coupling
The flexible coupling with integral clamp attachment had a set screw at each end to
clamp it to the motor shaft and the rotor. It was manufactured by ABSSAC Company,
and was of type WAC50-20mm-16. The outside diameter was 50 mm and the length
54 mm, and it had standard bore diameters of 20 mm and 16 mm, similar to the rotor
and motor shaft diameters respectively, as shown in Figure 3.14. The specifications of
the coupling were as follows: shaft misalignment angular 5 degrees, parallel offset 0.25
mm and axial motion at ± 0.25 mm. It was made of 7075-T6 aluminium alloy and had a
minimum weight of about 232 grams and an inertia temperature ranging between -40
and +100C. The torque ranged between 0.59-20 Nm, the torsional stiffness rate was
0.31 degree/Nm, and the speed was designed to go up to 10000 rpm [125]. The flexible
couplings were designed to transmit torque while permitting some radial, axial, and
angular misalignment. Flexible couplings were capable of accommodating angular
misalignment of up to 5 degrees, as well as some parallel misalignment between the
motor shaft and the rotor [125].
Figure 3.14: Flexible coupling: (a) integral clamp attachment methods, (b) Unequal diameter
shafts [125]
The advantage of using a flexible coupling between the motor shaft and the rotor was
that it accepted both parallel and angular misalignment. The coupling was connected
between the motor shaft and the front end of the rotor as shown in Figure 3.15.
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Figure 3.15: Flexible coupling installation with the motor shaft and the end of the rotor
3.5.2.6 Foundation table
The foundation table of the rig at the Dynamics Lab was made from bricks and had a
steel plate on its top surface with the dimensions of 1736 x 1242 x 30 mm for its length,
width and thickness, respectively. A 12 mm thickness vibration isolating material was
squeezed in between the steel plate and the steel frame of thickness of 50 mm, as shown
in Figure 3.16. The surface of the rectangular plate placed on top of the table was
machined to a smooth flat surface so that the experimental setup could be securely
attached.
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Figure 3.16: Rig foundation table
The rectangular base plate for the experimental rig was 1500 x 150 x 20 mm in length,
width and thickness, respectively, and was secured tightly to the upper plate of table by
means of four bolts. The foundation of the motor and bearings pedestals was placed on
the smooth machined surface of this plate.
The rectangular plate had holes drilled at pre-defined locations, allowing the motor and
bearings foundation to be attached as required, as shown in Figure 3.16, allowing the
length of the rig to be increased or decreased.
3.5.2.7 Rig casing
A metal steel casing with two rectangular transparent windows was designed to cover
the test rig as a safety precaution. The transparent windows were designed with one at
the top and one at the front to allow monitoring of the rotating parts during tests, as
shown in Figure 3.17. The cover had four hinges on the back, to allow ease of handling
and access. An electrical safety switch was incorporated, which was attached to the
hinges allowing the motor to be switched off if the cover was opened by more than 7
Vibration isolating material
Brick base
Steel plate
Rectangular plate
Steel frame
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degrees in relation to the surface of the rigid table, in which case the circuit was cut off
for safety reasons.
Figure 3.17: Rig safety cover with (a) Side monitoring windows, (b) Top monitoring windows,
(c) Electrical safety switch
3.5.3 Instrumentation
The instrumentation, including measurement and rig control devices used in the rig-
based experiments, are discussed in this section.
3.5.3.1 Incremental rotary encoder
A rotary shaft encoder was used to extract signals from the main rotor during rotation.
The encoder used was manufactured by Kübler (https://www.kuebler.com/) and is a 9
bit magnetic signal turn encoder with an AC/DC power supplier converter. The
technical specifications of the encoder are listed in Table 3.5. The encoder shaft of
4 mm diameter was attached to the free end of the rotor via an aluminium connector and
it rotated with the main rotor, as shown in Figure 3.18. The data was collected from the
encoder via a data acquisition card which was connected to the PC, on which special
LABVIEW software recorded and saved the data, which was then stored on the PC. A
typical rotary encoder used for the experiments, which generates 360 square pulses for
every complete rotation of the shaft, is shown in Figure 3.19.
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Table 3-5: Specification of the encoder
1 Maker Kübler
2 Type 2400
3 Output signal Push-Pull
4 Pulses per revolution 360
4 Power supply 5-24 V, DC
5 Maximum measured speed 12000 rpm
6 Pulse shape Rectangular
7 Maximum switching frequency 160 kHz
8 Weight 60 g
Figure 3.18: Location of encoder at the end of the rotor
Figure 3.19: Rotary shaft encoder
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3.5.3.2 Optical tacho sensor
An optical tacho sensor used in the experiments was an S51-PA-2-A00-NK as shown in
Figure 3.20, and the sensor was manufactured by Farnell (http://uk.farnell.com/). Tacho
specifications are listed in Table 3.6.
Table 3-6: Specifications of the Tacho sensor
1 Maker Farnell
2 Type S51-PA-2-A00-NK
3 Maximum Switching frequency 500 Hz
4 Sensing Distance range 0.1 – 3 m
5 Response time 1 ms
6 Weight 25 g
7 Supply Voltage DC Range 10-30 V
Figure 3.20: Optical tacho sensor
It was placed on the fore section of the motor, pointing towards the motor. There was
reflective marker tape on part of the motor shaft from which the tacho sensor picked up
reflected optical signals to measure the rotating shaft speed. This device provided a
quick and convenient way of monitoring motor rotating shaft speed.
3.5.3.3 Tachometer screen
A tachometer screen DFM 125 was manufactured by Control Ability
(www.controlability.com). This was used in the experiments to obtain pulse signals
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from the rotating machine via a tacho proximity sensor to calculate and display the
approximate motor rotating shaft speed being monitored. It had an LED digital display,
giving clear indication of the RPM shaft rotating speed values. The tachometer also had
a front panel keypad where the user could readily change the programme to meet
operational requirements, as shown in Figure 3.21. By using the front panel features, the
following could be carried out as required; (i) auto calibration, (ii) fixed or automatic
range changes, (iii) simple scaling functions, (iv) display of update and timeout
facilities, and (v) provision of fixed or floating decimal points.
Figure 3.21: Tachometer screen
3.5.3.4 Speed controller
The speed controller used in this setup was a Newton Tesla (http://www.newton-
tesla.co.uk/) CL750, as shown in Figure 3.22. This provided direct control of the motor
speed by using control buttons for: starting and stopping motor operation, and also
forward or reverse rotation direction settings. However, increasing motor speed (to
reach 3000 rpm) could be controlled through a computer using special software. In
addition, the software could also be used to operate the motor for run-up or run-down
operations using fixed speed rate. The speed controller specifications are listed in Table
3.7.
Table 3-7: Specifications of the speed controller CL750
1 Maker Newton Tesla
2 Model CL750
3 Rated power 720W/1 HP
4 Speed range 0 - 1800 rpm
5 Speed direction Forward / Reverse Selector switch
6 Input Power 230V AC, Three Phase
CHAPTER 3 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
78
7 Motor Output Power 0-230V AC, Three Phase
Figure 3.22: Speed controller
3.5.3.5 Data acquisition
The data were recorded on the PC using a National Instruments (http://uk.ni.com/) NI
USB-6221 eight-channel board, as shown in Figure 3.23. The NI USB-6221 board 16
bit M series could take up to 250,000 samples per second, and had a maximum clock
rate of 1 MHz and maximum source frequency of 80 MHz.
Figure 3.23: National Instruments NI USB-6221 board
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3.5.3.6 Accelerometer
Accelerometers are used to measure the acceleration signals and are commonly used to
measure vibration. The accelerometers used in this study were PCB Piezoelectric
(http://www.pcb.com/) accelerometers, as shown in Figure 3.24, a sensing transducer
that generated an electrical output signal proportional to the acceleration aspect of
object motion (vibration). This type of accelerometer is capable of measuring very high
acceleration transients, and is widely used in rotating machinery vibration measurement.
Figure 3.24: Accelerometer
3.5.3.7 Signal conditioning unit
A 4-channel signal conditioner was used in the research experiments, which was
manufactured by PCB Piezotronics (http://www.pcb.com/), Model 482C, 15 series, as
shown in Figure 3.25. The device was used to magnify the acceleration vibration signals
coming from accelerometers attached to the bearings pedestals and/or on the casing and
to transfer the data to a data acquisition board. The conditioning frequency response
range was between 0.05 Hz to 50000 Hz.
Figure 3.25: Front and back panel of 4-Channel signal conditioner PCB Piezotronics, 482C
CHAPTER 3 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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3.5.3.8 Measurement scheme
The schematic diagram of the measurements system used on the test rig, when the
experiments were conducted for the purpose of identifying and detecting blade vibration
due to various blade health conditions in order to use them in BHM, is given below. The
schema consists of the measurement devices of pickups, transducer system additions to
rig controls and signal treatment devices. Figure 3.26 illustrates a schematic
representation of the rig measurements and data acquisition system, which includes the
following devices:
1- One rotary shaft increment encoder with a power supply unit to measure the
shaft torsional vibration signals.
2- Four accelerometers attached on the bearings pedestals, 2 on the vertical and 2
on the horizontal direction for both the front and back bearings, to pick up on-
bearing vibrations.
3- One accelerometer attached on the casing cover, aligned towards the rotating
blades to pick up on-casing vibrations.
4- One tacho sensor and its tachometer screen, which was a sensor set towards the
rotating motor shaft to monitor motor rotating speed, which was displayed on
the tachometer display screen.
5- One data acquisition board of 8 channels, to show and record the signals
obtained from the encoder, tacho sensor and the accelerometers, and to sort all
data on the PC for further signal processing.
6- One or two 4-channel signal conditioner(s), depending on requirement, were
used to magnify the acceleration vibration signals picked up by the
accelerometers. In case of using more than 4 accelerometers (i.e. 4
accelerometers on the bearing pedestals, 2 in vertical direction and 2 in
horizontal direction, in addition to 1 accelerometer on the casing to measure case
vibration), another 4-channel signal conditioner was used at the same time.
7- One speed controller used to control the motor operation, motor speed levels and
rotation direction and speed run-up or run-down directly or through a computer
via special software.
8- One personal computer system for computerised data processing and control of
the motor speed. The PC was also used for data analysis software and storage of
data.
CHAPTER 3 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 3.26: Schematic diagram of the rig data acquisition system
3.6 Modal testing
Modal analysis refers to the process of estimating the modal properties, such as
resonance frequencies, stiffness, damping and mode shapes of a structure.
Generally, the dynamic behaviour of a structure can be predicted from knowledge of its
modal properties. In fact, the response of a linear system to excitation can be expressed
as a sum of the contributions from all the modes of the structure.
The dynamic parameters of the structure, such as dynamic stiffness and damping
constant, are vital technical information needed in engineering analysis and for rotating
machinery design. Those parameters are required for the finite element modelling as
well as for numerical simulation to predict the structural response to dynamic loading.
The modal testing is based on vibration responses of the structures which are used to
identify the dynamic performance and responses of structures. By using frequency
response functions (FRF) [126] to extract essential modal parameters, estimating modal
parameters in the initial design stages of the machinery is known to avoid any resonance
or near resonance effects during the running time of the machine. This is important to
CHAPTER 3 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
82
minimise fatigue cycles which cause destruction in the structure, and to increase the life
duration of the machine [126]. The inherent information in FRF data is also used to
identify the location and severity of damage in structures by knowing the natural
frequency range for every component of structure as a fault detection method.
3.6.1 Long and short blades FRF
The modal testing for the blades and shaft was conducted on the test rig for the eight
blades for both long and short bladed discs in order to determine the blades’ natural
frequencies by using the FRF test [126]. A schematic diagram and a photograph of
experimental procedures conducted for the blades modal test are presented in Figures
3.25 and 3.26 respectively. Each blade was excited using an instrumented impact
hammer (Type PCB-0860C03) and the vibration responses were measured using a tiny
accelerometer (Model 352C22, M/s PCB) weighing just 0.5 g. The responses were
calculated using a signal analyser unit and the data were recorded using a computer. The
blades natural frequencies were identified using the FRF calculated from the measured
force and acceleration data. The experimentally identified first natural frequencies for
each blade of the long and short blades after full assembly in the rig are listed in Table
3.8. A small deviation in blade(s) natural frequencies of 3.12 and 5.42 Hz for long and
short blades, respectively, showed the presence of blade mistuned effect, possibly due to
small deviation in blade manufacturing and/or fitting.
Table 3-8: Experimentally identified long and short blades first natural frequency
Long Blades Short Blades
Blade No. Natural frequency (Hz) Blade No. Natural frequency (Hz)
1 123.75 1 233.30
2 126.25 2 231.80
3 125.00 3 232.20
4 127.50 4 241.00
5 128.75 5 236.90
6 123.75 6 238.00
7 125.00 7 236.60
8 125.00 8 234.90
± % deviation 125.62 (± 3.12) Hz 235.58 (± 5.42) Hz
CHAPTER 3 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 3.27: Schematic diagram of blades modal testing
Figure 3.28: Photograph of modal testing setup
Typical FRF plots of magnitude, phase and imaginary part show blade frequency peaks
at 127.50 Hz and 234.90 Hz for blade 4 of the long blades and blade 8 of the short
blades respectively, as shown in Figures 3.29 (a-c) and 3.30 (a-c) respectively.
CHAPTER 3 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 3.29: Typical FRF plots for a long blade (blade 4) first natural frequency at 127.50 Hz:
(a) Amplitude, (b) Phase, (c) Imaginary part
Figure 3.30: Typical FRF plots for a short blade (blade 8) first natural frequency at 234.90 Hz:
(a) Amplitude, (b) Phase, (c) Imaginary part
0 50 100 150 200 2500
5000
10000
15000
X: 127.5
Y: 1.103e+04
Frequency (Hz)
Acc
el./
Forc
e (g
/N)
0 50 100 150 200 250-180
-90
0
90
180
X: 127.5
Y: -79.31
Frequency (Hz)
An
gle
(d
egre
e)
0 50 100 150 200 250-15000
-10000
-5000
0
5000
X: 127.5
Y: -1.084e+04
Frequency (Hz)
Acc
el./
Forc
e (g
/N)
0 50 100 150 200 250 300 350 4000
5000
10000
X: 234.9
Y: 9652
Frequency (Hz)
Acc
el./
Forc
e (g
/N)
0 50 100 150 200 250 300 350 400-180
-90
0
90
180
X: 234.9
Y: -110.6
Frequency (Hz)
An
gle
(d
egre
e)
0 50 100 150 200 250 300 350 400-10000
-5000
0
5000
X: 234.9
Y: -8598
Frequency (Hz)
Acc
el./
Forc
e (g
/N)
(b)
(a)
(c)
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3.7 Summary
An FEM was described for primary and final design of shaft-disc-blades assembly
(main core of the rotating components of the rig) to extract the optimum dimensions of
the experimental rig. The experimental rig manufacture based on the dimensions
obtained by the FEM was also described including the details of the parts and
instrumentation which were used to build the rig. Details of modal testing using FRF for
the long and short blades in order to determine blades and shaft natural frequencies were
also presented in this chapter.
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CHAPTER 4
BLADE DIAGNOSIS USING ON-BEARING VIBRATION (OBV)
4.1 Introduction
In this chapter, details are presented of the results of tests of OBV signals extracted
from the measured acceleration data during machine run-up to understand the blade
vibration behaviour and then to explore the possibility of reliable assessment of blade
health condition. A signal is measured from on-bearing pedestals using accelerometers;
the measured vibration data are analysed by computing the responses at different engine
orders (EOs) related to the blade resonance frequencies and their higher harmonics to
understand the blade(s) dynamic behaviour. A series of experiments were carried out on
a test rig on two types of 8-bladed discs, with long and short blades, as shown in Figure
3.9, in three different cases of blade fault conditions; (1) healthy with mistuned effects,
(2) blade root looseness and (3) blade(s) with crack(s). These conditions are often
observed in practice in rotating machines, like steam turbines and aero-engines, and
hence early and reliable detection of blade fault conditions is very important to reduce
machine down time and maintenance costs and to maintain overall safety. The extracted
acceleration signal data from the front and/or the back bearing pedestals during machine
run-up were then order tracked at different engine order (EO) accelerations so that the
presence of blade resonance, their higher harmonics and dynamics behaviour could be
analysed and compared to be used for blade health monitoring (BHM).
4.2 Blade faults simulation
Three different blade faults were simulated for the experiments. These conditions are:
(a) Case 1: healthy blades with mistuned effects, (b) Case 2: blade root looseness and
(c) Case 3: crack(s) on blade(s). These are summarised in Table 4.1 and also discussed
in the following sections. Figure 4.1 illustrates the faulty blade(s) position with respect
to the tacho sensor location. The illustrated blade fault cases were tested for long blades
and only some blade fault cases were tested for short blades (Table 4.1). The reason for
testing only some cases for short blades is related to the results obtained from the
experiments conducted on long blades, which gave very similar results in blade fault
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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cases. Therefore, short blades experiments, which were performed later, were only
limited to two types of blade faults in order to compare them to healthy case 1; these
cases were: a crack on one blade (Case 3(i)) and cracks on two blades (Case 3(ii)). The
results of this study represent a proof of principle and time constraints played a large
role in the experimental design.
Table 4-1: Cases of blade faults conditions
Case Description Long Blade No. Short Blade No.
1 Healthy but mistuned All blades healthy with
mistuned effects
All blades healthy with
mistuned effects
2 Blade root looseness
(i) Blade No. 1 Not tested
(ii) Blade No. 5 Not tested
3 Crack(s) on blade(s)
(i) Blade No. 4, cracked at
22 mm from blade root
(i) Blade No. 5, cracked at
26 mm from blade root
(ii) Blade Nos. 2 & 4 (Two
cracked blades together)
(ii) Blade Nos. 5 & 7 (Two
cracked blades together)
(iii) Blade No. 2, crack at
37 mm from blade root Not tested
(iv) Blade No. 4, crack at
22 mm from blade root,
after 100 minutes of
machine operation
Not tested
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 4.1: Schematic diagram of the blade positions with respect to the tacho sensor
4.2.1 Healthy with mistuned effect
The healthy blades with mistuned effect (Case 1 as per Table 4.1) were tested initially.
The variation in the blades first natural frequencies is listed in Table 3.8. This case
represents the most likely fault condition for healthy blades in any real rotating machine
such as steam turbines and gas turbines.
4.2.2 Root looseness
If the blades are not properly mounted and assembled on the shaft disc then the blades
roots may be loose and that could result in rubbing during machine operation which
may lead to eventual failure in operation. Hence, this root looseness fault is simulated
by putting 2 free loose washers on the roots of randomly selected blades 1 and 5
separately as per Case 2 (i) and (ii) for the long blades as listed in Table 4.1. Figure 4.2
shows a photograph of the blade root looseness fault simulation.
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Figure 4.2: Simulation of long blade root looseness fault
4.2.3 Crack simulation
A small cut of 0.6 mm width was made using a thin saw on 4 blades, 2 for each type of
blades, long and short, but at different locations. Photographs of crack simulation details
for long and short blades are shown in Figures 4.3 (a) to (d) and 4.4 (a) to (d),
respectively. A very thin metal sheet was placed in the cut of the cracked blade(s) using
adhesive glue on only one side of the cut so that breathing (opening and closing) of the
crack on the blade could be realized during vibration. The cracks made on the 2 long
blades at different locations are shown in Figure 4.5 (a). In addition, the cracks made on
the 2 short blades at different locations are shown in Figure 4.5 (b). The cracked blades
were randomly selected for Case 3 tests on cracked blades; for long blades, blade 2 and
blade 4 were selected and for short blades, blade 5 and blade 7 were selected, as per
Table 4.1. The blades are numbered with respect to their position from the tachometer,
as shown in Figure 4.1. For the long blades, a total of 4 tests were conducted for the
cracked blade cases 3 (i) to 3 (iv) in Table 4.1, to understand the dynamic behaviour of
cracked blade(s) in different combinations; (i) and (iii): crack on a single blade but at
different locations, (ii): cracks on two blades simultaneously and the impact on the
dynamic behaviour when there is a cracked blade, and (iv): one cracked blade is tested
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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after 100 minutes of machine operation. Moreover, two tests were conducted on the
case of cracked short blades as per Table 4.1, as in case 3; (i): one cracked blade on the
bladed disc, and (ii): cracks on 2 blades together on the bladed disc. These six tests were
done for one cracked blade and two cracked blades on the bladed disc for both long and
short blades to understand the changes in the dynamic behaviour of the different
cracked blades, as in case 3 in Table 4.1.
Figure 4.3: Long blade crack simulation: (a) front side of a crack, (b) back side of a crack, (c) a
very thin metal sheet in the crack, and (d) cracked blade location on the bladed disc on the rig
Figure 4.4: Short blade crack simulation: (a) crack dimensions, (b) cracked blade with shim
location on the bladed disc on the rig, (c) shim on crack adhesive side and (d) shim on crack
non-adhesive side
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Figure 4.5: Cracks on the blades at different locations: (a) long blades (b) short blades
A photograph of cracked blade(s) on the bladed disc is also included in Figure 4.6 and
Figure 4.7 for both long and short blades, respectively.
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Figure 4.6: Crack on one long blade on the bladed disc
Figure 4.7: Crack on two short blades on the bladed disc
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4.3 Computation of engine order tracking (EOT)
Engine order tracking method is a commonly used in rotating machinery vibration
analysis, which is based on the orders representing rotational speed harmonics. It is
considered a useful technique for monitoring the condition of rotating machines, which
can be easily carried out using shaft rotational speed related vibration signals.
Conditions that can be monitored include shaft defects, blade cracks, gearbox teeth
faults and bearing wear. Order tracking analysis in its simple form is performed by
using a constant sample rate of vibration signal and then the constant sampling intervals
are synchronised with shaft rotational speed signals extracted using a tachometer. After
that, a fast Fouriertransform (FFT) is computed by generating waterfall or contour plots
for frequency data with amplitude and phase data to determine the engine orders at
different run-up speeds. Finally, the order tracking is carried out at different speeds
related to engine orders. Rotating speed is measured using tachometer pulses when the
machine is in non-stationary operation during machine run-up or coast-down.
In this study, engine order tracking was used as a vibration analysis method for BHM in
order to determine blade behaviour under different conditions of blade health during
machine run-up using vibration signals. The EOT was computed based on the following
steps:
(i) Selecting real-time run-up operation by matching signals from the tachometer
(rotating shaft speed) with run-up speed signals as shown in Figure 4.8 (a) and (b),
which are determined at time (t1), i.e. when run-up is starting and time (t2) when run-up
is ending, as shown in Figure 4.8 (b), for a total run-up time of 30 seconds.
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 4.8: Tachometer signals: (a) rotating shaft speed, (b) blades EO1 of run-up speed (1X),
and (c) zoom view of (a)
(ii) Sampling vibration signal at fixed time intervals, by dividing the signal into
small windows segments of time; in every window, the starting signal time is (tn-1) and
the ending time is (tn) for time intervals (t1, t2, t3,…….,tn). The mean time (tmean) is then
0 5 10 15 20 25 30 35 40-5
0
5
10
Am
pli
tud
e, V
olt
ag
e (
V)
Time (s)
0 5 10 15 20 25 30 35 400
10
20
30X: 35.95
Y: 29.5
Time (s)
Sp
eed
(R
PS
)
X: 5.935
Y: 9.841
4.0 4.4 4.8 5.2 5.6 6.0 6.4 6.8 7.2 7.6 8.0-4
-2
0
2
4
6
8
Time (s)
Am
pli
tud
e, V
olt
ag
e (
V)
(a)
(c)
(b)
t1=5.935 sec t2=35.95 sec
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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calculated between every starting and ending time point for all the windows, where tmean
is calculated using the equation:
𝑡𝑚𝑒𝑎𝑛 =𝑡𝑛−1+ 𝑡𝑛
2 ……………..(4.1)
wheren=1,2,3,……..,N.
The run-up speed values (in rpm) are then determined for every tmean for all the windows
using the tachometer run-up signal corresponding to tmean. Figure 4.9 illustrates a typical
measured vibration signal.
Figure 4.9: Measured vibration signal: (a) acceleration raw signal and (b) zoomed view of (a)
0 10 20 30 40 50-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
Time (s)
Am
pli
tud
e, A
ccele
ra
tio
n (
m/s
2)
10 10.005 10.01 10.015 10.02-0.02
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
0.02
Time (s)
Am
pli
tud
e, A
ccele
ra
tio
n (
m/s
2)
(a)
(b)
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(iii) Synchronising the sampled rate of the vibration signal with constant shaft
rotational speed intervals using the tachometer signals.
(iv) Calculating the frequency domain using FFT for every time averaging segment
signal by generating waterfall or contour plots for frequency with rotating speed
(rpm) and amplitude using signal processing analysis performed using MATLAB
software to determine speed orders, as shown in Figure 4.10.
Figure 4.10: Typical contour plot of speed orders for on-bearing vibration response in
horizontal direction
(v) Extracting the speed order components using order tracking for the EOs related to
shaft rotating speed (which is calculating order amplitude and phase for the
vibration signal). Once blade resonance (BR) is obtained from contour plots, as
shown in Figure 4.10, order tracking can then be carried out for different speed
orders. The EO components related to blades’ natural frequency can then be
determined. This is obtained in this case for long blades at EO5 (5X) related to a
blade natural frequency of 122 Hz (5 x 24.40 Hz) as shown in Figure 4.11.
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Figure 4.11: Typical measured on-bearing vibration data for engine order spectra of EO5 of
healthy blades
The OTA signal processing technique transforms the revolution domain to an order
spectrum, rather than a frequency spectrum (FFT), providing the signal amplitude and
phase as a function of the harmonic order related to the rotation frequency. The problem
in using FFT with variable machine speed is the resultant smearing of frequency
components (due to overlap), and therefore it is a more favourable method with constant
rotation speed. Order tracking is able to show clear signal peaks in the order domain,
providing good tracking of speed harmonics in rotating machines during machine run-
up or coast-down and simplified use for diagnosis related to rotational speed orders in
rotating machines. OTA was used in this study as a signal processing tool for
diagnosing blade health conditions, where all signal amplifications can be seen clearly
related to different blade resonance frequencies. The blades in these tests have multiple
peaks in the BR region related to the number of blades rather than a single peak in other
applications. Hence, EO is an effective and powerful tool when used with applications
which result in multiple peaks of signal amplifications in the frequency region, such as
60 80 100 120 140 1500
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
-4
Frequency (Hz)
EO
5, A
ccele
ra
tio
n (
m/s
2)
BR region
Sh
aft
mo
de
regio
n
Sh
aft
to
rsio
nal
mod
e re
gio
n
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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blades and gearboxes, and for the detection of faults in these applications, particularly
when machine rotational speed changes during run-up and coast-down operation.
4.4 Long blades experiments and data analysis
In this research study, the dynamics for both blade condition cases, healthy and faulty
blades, are studies conducted on a small experimental rig using OBV measured data
using accelerometers on the bearing pedestals on the front and rear bearing. The
instrumentation needed for OBV measurement is simple, cheap, non-intrusive and
indirect. The experiments are conducted on a test rig having 8 long bladed discs for the
blade condition cases of: healthy blades with mistuned effects, blade root looseness and
cracked blade(s), and the data are recorded using a computer during machine run-up for
further signal analysis. The measured vibration data are analysed by computing the
responses at different Eos related to the blade resonance frequencies and their higher
harmonics to understand the dynamic behaviour of blades in healthy and faulty cases.
The purpose of the study is to determine whether the method is effective for the purpose
of BHM, and also to compare results with the experiments of short blades used for the
same purpose.
4.4.1 Data analysis
The OBV signals during machine run-up are obtained. In addition, tachometer signals
relating to machine run-up speed are also collected as shown in Figure 4.8 (b).
Vibration signals are collected from both the front and back bearing pedestals in vertical
and horizontal directions, from four locations on the rig using 4 accelerometers; the data
are analysed for all four signals. The results of four vibration signals are similar, and
therefore in this chapter, the data from the front bearing in vertical direction are
presented. Short time Fouriertransformation (STFT) is carried out in order to discover
the BR frequency region for the on-bearing acceleration signals; this is illustrated in
Figure 4.10. Once the BR and its higher harmonics are obtained, order tracking at
different EOs can be carried out. Contour plots show the BR peaks (1xBR) and the
related higher harmonics (2xBR, 3xBR….).Hence, order tracking at different EOs is
then carried out using the tachometer signal of the OBV. The order tracking of
acceleration signals at EO5 and EO10 clearly shows the BR region and its higher
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
99
harmonics, respectively. Typical EO5 for healthy blades is shown in Figure 4.11. Such
order tracking can typically be shown as in Figures 4.14 to 4.16 for EO5 and Figures
4.17 to 4.19 for EO10. Figures 4.14 to 4.19 can be used to compare the order tracked
responses at EO5 and EO10 in the BR region for OBV signals for the cases: healthy
blades, blade looseness and cracked blade conditions. The location of the
accelerometers for data pickup from the on-bearing is in vertical and horizontal
directionsasshowninFigure4.12.Also, thefrequencydomainofvibrationsignals is
performedusingFFT; this provides the blade natural frequency (1xBR) and its higher
harmonics (2xBR) as shown in Figure 4.13.
Figure 4.12: Accelerometer location in relation to the on bearing pedestals in the vertical and
horizontal directions
Figure 4.13: Typicalamplitudespectrumforlongblades
0 100 200 300 400 500 600 700 800 900 10000
2
4
6
8
10
12
14
16
18
X: 386.4
Y: 7.112
Frequency (Hz)
Am
pli
tud
e
X: 258.2
Y: 10.09
X: 128.2
Y: 16.84
X: 644.5
Y: 3.252
X: 772.7
Y: 3.71X: 900.9
Y: 2.123
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4.4.2 Observations and results
From the results observed in Figure 4.14 representing Case 1: healthy blades with
mistuned effects, amplification at around frequency 115 Hz (23 Hz x 5) to 135 Hz (27
Hz x 5) confirms the excitation of BR during machine run-up, based on the OBV data
collected using an accelerometer on the bearing pedestals to measure the vibrations. The
amplification of the BR region seems to be in a banded form due to the mistuned effect.
The higher harmonics of the banded BR region (2xBR) was also observed in EO10 data
shown in Figure 4.17 for healthy blades with mistuned effect. The mistuned effect in the
blades could possibly be the reason for the appearance of this amplification in the higher
harmonics even for the healthy case (Case 1) as per Table 4.1.
Case 2: blade root looseness for blade 1. The addition of 2 loosely held washers at the
root has not affected the natural frequency of blade 1 significantly but its EO5 and
EO10 responses, shown in Figures 4.15 and 4.18 respectively, show significantly
different behaviour compared to healthy blades in Case 1. The 1xBR region shows
distinct multiple peaks instead of a single banded peak around the 1xBR region for Case
1 for healthy blades as seen in Figures 4.14. Also, the higher harmonics 2xBR in
Figures 4.18 also show multiple peaks.
Case 3: cracks on blades 2 and 4. From on-bearing acceleration data shown in Figures
4.16 and 4.19, a single banded peak can be seen in blade(s) harmonics regions (1xBR
and 2xBR) related to the EO5 and EO10 acceleration responses, respectively. However,
this is very different from Case 1 of healthy blade condition. This can show that OBV
are useful as an indication of BHM. Figures 4.14, 4.15 and 4.16 are combined in
Figure A1 (in Appendix A) showing on-bearing vibration data analysis for EO5 in blade
health condition cases: healthy blades, blade root looseness and cracks on two blades. In
addition, Figures 4.17, 4.18 and 4.19 are combined in Figure A2 (Appendix A) for
EO10.
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
101
Figure 4.14: Measuredon-bearingaccelerationdataforengineorder spectra of EO5 for healthy
blades with mistuned effects (Case 1)
Figure 4.15: Measuredon-bearingaccelerationdataforengineorderspectra of EO5 for blade
looseness (Case2 (ii): Blade no. 5)
115 120 125 130 1350
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
-4
Frequency (Hz)
EO
5, A
ccele
ra
tio
n (
m/s
2)
115 120 125 130 1350
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
-4
Frequency (Hz)
EO
5, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
102
Figure 4.16: Measuredon-bearingaccelerationdataforengineorder spectra of EO5 for two
cracked blades (Case3 (ii): Blades no. 2 and 4)
Figure 4.17: Measuredon-bearingaccelerationdataforengineorderspectra of EO10 for
healthy blades (Case1)
115 120 125 130 1350
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
-4
EO5 Frequency (Hz)
Accele
rati
on
(m
/s2)
(e)
220 230 240 250 260 2700
0.5
1
1.5
2
2.5
3x 10
-4
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
103
Figure 4.18: Measuredon-bearingaccelerationdataforengineorder spectra of EO10 for blade
looseness (Case 2 (ii): Blade no. 5)
Figure 4.19: Measuredon-bearingaccelerationdataforengineorderspectra of EO10 for two
cracked blades (Case 3 (ii): Blade no. 2 and 4)
220 230 240 250 260 2700
1
2
x 10-4
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
220 230 240 250 260 2700
0.5
1
1.5
2
2.5
3x 10
-4
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
104
4.5 Short blades experiments and data analysis
The same procedures are discussed in Section 4.2 with short blades using the
dimensions and properties listed in Table 3.3. The BR region related to the blades’
natural frequency is observed between 220 and 260 Hz, which is banded due to the
blades’ mistuned effects.
4.5.1 Experimental setup
The experiments are conducted during the machine run-up from a speed of 600 rpm (10
Hz) to 1800 rpm (30 Hz). The run-up rate was kept constant at 40 rpm/s. The data were
then collected at 30000 samples/s and the data were stored on a personal computer (PC)
for further signal processing analysis using MATLAB.
Three different conditions of short blades faults were simulated in the experimental
tests. These conditions of blade health conditions are: healthy with mistuned effects, a
crack on one blade and cracks on two blades as listed in Table 4.1.
4.5.2 Data analysis
The extracted OBV signals are processed with different EOs of acceleration to
understand the appearance of blade resonance and blade dynamics behavior. The
vibrationresponseoftheaccelerationsignalrelatedtoOBVisshowninFigure4.20.
Figure 4.20: Typical measured on-bearing acceleration signals at machine run-up from 600 to
1800 rpm
0 5 10 15 20 25 30 35 40 45-1
-0.5
0
0.5
1
1.5
2
Time (sec)
Accele
ra
tio
n (
m/s
2)
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
105
4.5.3 Observations and results
Fromtheresponseresultsobtainedforshortblades,neitherEO10norEO20containthe
bladeresponsesofhealthybladeswithmistunedeffectoronecrackedblade,asshown
inFigures 4.21 and4.22 forEO10 andFigures 4.24 and4.25 forEO20.However, a
clearblade response canbe seen inCase3, twocrackedblades, inbothEOplots, as
showninFigures4.23and4.26.BRcanbeobservedclearlyinCase3.Thezoomview
of Figures 4.21 to 4.23 in the frequency range 220 Hz to 260 Hz can be made to
illustratethebladeresonance(1xBR)regionanditshigherharmonics(2xBR),asshown
in Figures 4.24 to 4.26 for all cases for the purpose of clear observation and
comparison.
Case1:healthywithmistunedeffects; it isobservedfromFigure4.21 that inCase1,
healthybladeswithmistunedeffects,on-bearingvibrationshowsnoamplificationinthe
BRregion.Also,thesameobservationofalackofbandedBRregion(2xBR)isseenin
its higher harmonics in the EO20, as shown in Figure 4.24 for healthy blades with
mistunedeffects.
Case2:onecrackonblade5;likewise,thereisnoclearbladeresonanceineitherEO10
orEO20responsesasshowninFigures4.22andFigure4.25respectively.
Case3:cracksontwoblades5and7;inthiscase,therearemultipledistinctpeaksin
thebladeresonance(1xBRand2xBR)regionsrelatedtoEO10andEO20responses,as
observed in Figures 4.23 and 4.26 respectively. Figures 4.21, 4.22 and 4.23 are
combined in Figure A3 (in Appendix A) showing on-bearing vibration data analysis for
EO10 in blade health condition cases: healthy blades, crack on one blade and cracks on
two blades. In addition, Figures 4.24, 4.25 and 4.26 are combined in Figure A4
(Appendix A) for EO20.
This shows that OBV measurement for short blades provided some good indication
bladeconditionsinthecaseofmorethanonecrackedbladeonthebladeddisc.Hence,
thepresenceofthedistinctmultiplepeaksintheBRregionanditshigherharmonicsin
thecaseof2ormorecrackedbladescanbeconsideredasausefulfeatureofBHM.
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
106
Figure 4.21: Measuredon-bearingaccelerationdataforengineorderspectraofEO10for
healthybladeswithmistunedeffects(Case 1)
Figure 4.22:Case3 (i): measuredon-bearingaccelerationdataforengineorderspectraofEO10
foronecrackedblade(Case 3 (i): Blade no. 4)
220 225 230 235 240 245 250 255 2600.016
0.018
0.02
0.022
0.024
0.026
0.028
0.03
0.032
0.034
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
220 225 230 235 240 245 250 255 2600.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.022
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
107
Figure 4.23:Measuredon-bearingaccelerationdataforengineorderspectraofEO10fortwo
crackedblades(Case 3 (ii): Blades no. 2 and 4)
Figure 4.24:Measuredon-bearingaccelerationdataforengineorderspectraofEO20for
healthybladeswithmistunedeffects(Case 1)
220 225 230 235 240 245 250 255 2600.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
440 450 460 470 480 490 500 510 5200.01
0.012
0.014
0.016
0.018
0.02
0.022
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
108
Figure 4.25:Case3 (i): measuredon-bearingaccelerationdataforengineorderspectraofEO20
foronecrackedblade(Case 3 (i): Blade no. 4)
Figure 4.26: Measuredon-bearingaccelerationdataforengineorderspectraofEO20fortwo
crackedblades(Case 3 (ii): Blades no. 2 and 4)
440 450 460 470 480 490 500 510 5200.004
0.006
0.008
0.01
0.012
0.014
0.016
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
440 460 480 500 5200
0.002
0.004
0.006
0.008
0.01
0.012
0.014
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 4 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
109
4.6 Summary
TheexperimentsdescribedinthischapterwerecarriedoutusingOBVmeasurementon
theexperimental test rig for longandshortblades for threecasesofbladeconditions;
healthywithmistunedeffects,bladerootloosenessandcrackedblade(s).Thedynamic
behavioursforbothbladesizeswerestudiedwhenbladeswereexcitedduringmachine
run-up.TheresultsindicatedthatthemeasuredaccelerationspectraatdifferentEosand
theirhigherharmonicsshowtheappearanceofBRwhenbladesareself-excitedduring
themachine’s transient operation in all simulated cases for longblades (healthywith
mistunedeffects,bladerootloosenessandcrackedblades),andthiswasonlyobserved
inthethirdcaseforshortblades(twocrackedblades). In addition, it was also observed
that OBV obtained from long blades gave better indication for the detection of blade
health conditions. Hence, the presence of the distinct multiple peaks in the BR region
and its higher harmonics can be considered as a useful feature of BHM. It was also
observedfromthistestthattheresultsrelatedtoshortbladesgavesomegoodindication
only in thecaseofmore thanone crackedbladeon thebladeddisc, and thismaybe
consideredasafeatureofBHM.
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
110
CHAPTER 5
BLADE DIAGNOSIS USING ON-CASING VIBRATION (OCV)
5.1 Introduction
This chapter summarises the results and observations of the tests for OCV signals
extracted using an accelerometer on the external side of the casing (safety metal cover)
towards the rotating blades to collect vibration signals which are affected by air
movement on the internal side of the casing while the blades are rotating in machine
run-up operation. This is carried out in order to understand the dynamic behaviour of
blade(s) and also to investigate the possibility of using this technique as a reliable
assessment for blade health conditions. The measured vibration data are then analysed
with further signal processing using MATLAB by computing blades’ responses at
different engine orders (EOs) related to the blade resonance frequencies and their higher
harmonics to understand and compare the results of blade(s) behaviour. A series of
experiments were conducted on a small rig with a 8-bladed discs, as illustrated in Figure
3.7, for three different cases of blade fault conditions; (a) Healthy blades with mistuned
effects, (b) Crack on one blade and (c) Cracks on two blades. These fault conditions
commonly occur in practice with real rotating machines such as steam turbines, gas
turbines, etc. Hence, early and reliable detection of blade health is very important to
reduce machine breakdown time and maintenance costs and to maintain overall safety.
5.2 Experiments on short blades
The specifications of the short blades used in these experiments are listed in Table 3.3.
The tests were carried out during machine run-up for three different cases of blade
health conditions as per Table 4.1. The modal testing also included the blades natural
frequencies as listed in Table 3.8.
5.2.1 Data analysis
The on-casing measured vibration signals were processed with different EOs to
understand the appearance of blade resonance (BR) and to investigate the blade(s)
dynamic behaviour. The location of the accelerometers that pick up data from the
externalsurfaceof thecasing(safetycover) is towardstherotatingbladesfrominside
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
111
therig.Aschematicdiagramofthelocationofon-casingmeasurementsonthetestrigis
shown in Figure 5.1 and photographs of on-casing measurements are shown in
Figure 5.2(a)and(b).ThemeasuredaccelerationresponsesignaloftheOCVresponse
isalsoshowninFigure5.3.
Figure 5.1:Aschematicofon-casingmeasurementsetup:(a)sideviewand(b)topviewofthe
rig
On casing accelerometer
towards rotating blades
View window Casing of
2 mm
thickness
8-Blades
On casing accelerometer
towards rotating blades
Bearing pedestal On-bearing accelerometer
(a)
(b)
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
112
Figure 5.2: On-casingmeasurementsetup:(a)topviewofrigshowingthebladesandtheon-
casingaccelerometer(b)rigbacksidecasing
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
113
Figure 5.3: Typical on-casing measured acceleration data during machine run-up speed (600 to
1800 rpm)
5.2.2 Observations and results
Measured data are analyzed using engine order tracking for vibration signals. The
resultsindicatedthattheEO10plotshowsonedistinctpeakaroundthefrequency238
Hz,whichconfirmsexcitationofBRduringmachinerun-up.Thisalsoappears in the
higherharmonics(EO20)inallcasesofbladeconditions:healthywithmistunedeffects,
acrackononebladeandcracksontwoblades.ThezoomviewsinFigures5.4to5.6in
thefrequencyrange220Hzto260Hzweremadetoillustratebladeresonance(1xBR)
regionand thehigherharmonics (2xBR)are shown inFigures5.7 to5.9 for all three
casesofbladefaults;thesefiguresareshowninordertoclearlyillustrateandcompare
theresults.TherewasnoamplificationoftheBRregion,asobservedinFigures5.4to
5.6forEO10,andonlyonedistinctpeakinthethreebladehealthcases.Thesameresult
wasalsoobservedinthehigherharmonicsofthebandedBRregion(2xBR)intheEO20
plots shown in Figures 5.7 to 5.9 for the three cases of blade health conditions.
Additionally, it can be seen from the results that there was not much change in blade
resonance behavior in EO10 and EO20 plots for the faulty cases (Case 3(i) and Case
0 5 10 15 20 25 30 35 40 45-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Time (sec)
Accele
ra
tio
n (
m/s
2)
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
114
3(ii)) compared with the healthy condition (Case 1). In other words, there are no
amplifications in BR in Cases 3(i) and (ii); as per Table 4.1. Figures 5.4, 5.5 and 5.6 are
combined in Figure B1 (in Appendix B) showing on-casing vibration data analysis for
EO10 in blade health condition cases: healthy blades, crack on one blade and cracks on
two blades. In addition, Figures 5.7, 5.8 and 5.9 are combined in Figure B2
(Appendix B) for EO20.
Figure 5.4: On-casing measured acceleration data for engine order EO10 spectra for healthy
blades with mistuned effects (Case 1)
220 230 240 250 2600
0.005
0.01
0.015
0.02
0.025
0.03
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
115
Figure 5.5: On-casing measured acceleration data for engine order EO10 spectra for a crack on
one blade (Case 3(i))
Figure 5.6: On-casing measured acceleration data for engine order EO10 spectra for cracks on
two blades (Case 3(ii))
220 225 230 235 240 245 250 255 2600
0.005
0.01
0.015
0.02
0.025
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
220 225 230 235 240 245 250 255 2600
0.005
0.01
0.015
0.02
0.025
0.03
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
116
Figure 5.7: On-casing measured acceleration data for engine order EO20 spectra for healthy
blades with mistuned effects (Case 1)
Figure 5.8: On-casing measured acceleration data for engine order EO20 spectra for a crack on
one blade (Case 3(i))
440 450 460 470 480 490 500 510 5200.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
440 450 460 470 480 490 500 510 5200
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 5 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
117
Figure 5.9: On-casing measured acceleration data for engine order EO20 spectra for cracks on
two blades (Case 3(ii))
5.3 Summary
Thedynamicbehaviourofshortbladesinrotationindifferentbladehealthconditions;
healthy with mistuned effects, a crack on one blade and cracks on two blades, was
studiedbyusingOCVmeasurementwhenbladesareexcitedduringmachinerun-up.It
was observed that themeasured acceleration spectra at differentEos and their higher
harmonicsshowtheappearanceofBRwhenbladesareself-excitedduringthemachine
run-upoperationinallcasesofbladeconditions.However,fromtheresults,itwasalso
observedthattherewasno change in the BR region of damaged blade(s) compared with
healthy ones, showing only a single distinct peak for all cases of blade health
conditions. This means that there was not much change in the dynamic behaviour of
blades during machine run-up for all three cases of blade health conditions for both
engine orders EO10 and EO20 with regard to BR regions, 1xBR and 2xBR,
respectively. Hence, based on these experimental tests, using the on-casing
measurement technique would not indicate any differences between healthy and cracked
blades and therefore would not be useful for blade health monitoring (BHM).
440 450 460 470 480 490 500 510 5200
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
118
CHAPTER 6
BLADE DIAGNOSIS USING SHAFT TORSIONAL VIBRATION (IAS)
6.1 Introduction
This chapter gives details of the results of the tests of shaft instantaneous angular speed
(IAS) signals extracted from the measured encoder data during machine run-up to
understand the blade vibration and to explore the possibility of reliable assessment of
blade health conditions. Transverse blade vibration is often transmitted to the shaft as
torsional vibration. A series of experiments were conducted on a test rig with two types
of 8-bladed discs (long and short blades) as shown in Figure 3.9 for three different cases
of blade fault conditions (1) healthy blades with mistuned effects, (2) blade root
looseness and (3) blade(s) with crack(s). These conditions are often observed in practice
with rotating machines like steam turbines, gas turbines, and heavy industrial machines.
Hence, early and reliable detection of blade fault conditions is very useful to reduce
machine down time and maintenance costs and to maintain overall safety. The extracted
IAS signals from the encoder raw data during machine run-up were order tracked at
different engine order (EO) speeds related to blade frequency so that the presence of
blade resonance, its higher harmonics and dynamic behaviour could be analysed.
Finally, a comparison was carried out to determine the differences in EO response for
long and short blades for the purpose of blade health monitoring (BHM).
6.2 Instantaneous angular speed (IAS) measurement method
An encoder that measures 360 pulses for every complete shaft rotation was used in this
experiment. To aid understanding of the IAS signal extraction from the measured
encoder pulse train, a simplified measurement scheme of the encoder and the pulse train
is shown in Figure 6.1. The sensor simply measures the gap between each tooth on the
gear during its rotation, which results in generating a pulse train of the measured gap
voltage against time, as shown in Figure 6.2. A magnification of the encoder raw signals
is presented in Figure 6.6. The extraction of IAS signal using the pulse train in Figure
6.1 is discussed in the following steps:
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
119
Figure 6.1: Schematic of the encoder pulse train
Figure 6.2: Measured encoder pulse train of the rotating shaft
Rotary encoder
generates 360
pulses per
revolution
Δ=1
Magn
itu
de,
(V
)
t4 t5 t3 t2 t1 Time, (t)
t4 t3 t1 t2
0 5 10 15 20 25 30 35
-3
-2
-1
0
1
2
3
Time (sec)
Vo
lta
ge (
v)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
120
(1) Each square pulse represents a tooth in the gear of the encoder; hence, the time
differences, 12 tt , 23 tt ,…., nn tt 1 ,……etc.representthetimeintervalsrequired
to cross the 1st, 2
nd,….,n
th tooth, respectively, which is written as
∆𝑡𝑛 = 𝑡𝑛+1 − 𝑡𝑛 …………….(6.1)
It is also assumed that the time interval for the nth
tooth is measured at time nT as
𝑇𝑛 =𝑡𝑛 + 𝑡𝑛+1
2 ……………….(6.2)
where n=1,2,3,4,5,…,N,thenumberofteethinthegearoftheencoder.
(2) The angular displacement, Δθ, for the each equally spaced tooth in the gear
wheel of the encoder can be written as
= 360/N = N
2 ………….….(6.3)
(3) Hence, IAS at time nT in terms of revolutions per second (rps) can be calculated
as
IAS (𝑓𝑠𝑛) at Tn =
(∆tn .2π)
1
∆tn.360……………….(6.4)
The extracted IAS signal from the measured pulse train from an encoder may appear as
shown in Figure 6.3 if the shaft is rotating at a constant speed. The ideal case is
expected to be when there is no fluctuation in speed during shaft rotation and/or no shaft
torsional vibration; i.e. Ntttt .....321 . However, in reality these time
intervals, nt , may not always be constant due to speed fluctuation and/or due to
presence of shaft torsional vibration and may be presented as shown in Figure 6.4. The
time vectors nT , and corresponding IAS, snf , may not be at equal time intervals, hence
the data are re-sampled with a uniform time interval before further signal processing.
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
121
Figure 6.3: Ideal shaft IAS showing a constant shaft speed without torsion
Figure 6.4: Typical shaft IAS showing the possibility of shaft torsion
6.3 Experiments conducted
Vibration experiments were carried out for all the cases listed in Table 4.1 (i.e., healthy
blades, root looseness and cracked blade(s)). Initially, the experiments were conducted
at different constant shaft speeds but the excitation from the shaft torsional vibration
was not observed to be significant; hence, further experiments were conducted during
the machine run-up from 600 rpm (10 Hz) to 1800 rpm (30 Hz). The run-up rate was
kept equal to 40 rpm/s. The analogue signals measured by the encoder and tachometer
sensor were then collected at 50000 samples/s and stored on the PC for further signal
processing. In this study, to ascertain the accuracy of results obtained using the rotary
encoder raw signals of shaft torsional vibration, the experimental tests were repeated
using different sampling frequencies of 30,000, 40,000 and 50,000 samples per second;
these gave the same results and did not give errors in signal. High sampling frequency
of more than twice the encoder tooth meshing frequency was used in the tests. The
(𝑓𝑠𝑛 ) is constant
Inst
an
tan
eou
s S
pee
d (𝒇𝒔𝒏
)
Time (Tn)
(𝑓𝑠𝑛 ) fluctuation
Inst
an
tan
eou
s S
pee
d (𝒇𝒔𝒏
)
Time (Tn)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
122
encoder tooth meshing frequency is calculated by multiplying the number of encoder
teeth (360) by the maximum running speed (30 Hz), which is equal 10,800 Hz.
Therefore, the use of high sampling frequency, i.e. more than twice the encoder tooth
meshing frequency of 10,800 Hz (> 21,600 Hz), was applied to overcome signal errors.
6.4 Data analysis
The measured tacho sensor and encoder data were analysed for all cases to extract the
shaft speed with time and the IAS signal with time. A typical shaft speed profile from
the tacho sensor and the raw signals from the encoder for the healthy blade condition
(Case 1) are shown in Figures 6.5 and 6.6, respectively.
Figure 6.5: A typical rotor speed profile for the machine during run-up
Figure 6.6: Typical magnified measured encoder raw signals
5 10 15 20 25 30 35 405
10
15
20
25
30
Time (Sec)
Sp
eed
(R
PS
)
20 20.0005 20.001 20.0015 20.002 20.0025 20.003 20.0035 20.004 20.0045 20.005-3
-2
-1
0
1
2
3
Time (sec)
Vo
lta
ge
(V
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
123
Figure 6.7 shows a typical EO5 response for Case 1 (healthy condition) for long blades.
This figure shows peaks at around 94 Hz (purple coloured region) and at 108 Hz (dark
green coloured region) related to shaft torsional resonance (with flexible coupling to the
motor) and the critical speed of the rig (the rig’s natural frequency). The figure also
shows the frequency of blade(s) with mistuned effects reflected by the peak between
118 and 135 Hz (light green coloured region).
Figure 6.7: A typical IAS response at e.g. EO5 for Case 1: healthy blades illustrating frequency
modes
Additionally, for the short blades, Figure 6.8 shows a typical EO10 response for Case 1:
healthy blade condition, which illustrates the peak of casing frequency mode at 180 Hz
(light blue coloured region) and the cluster of blade(s) frequency with mistuned between
210 and 260 Hz (light green coloured region). Also, the figure shows a shaft second
frequency mode peak at 262 Hz.
60 80 100 120 1400
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ati
on
(R
PS
)
(a)
To
rsio
na
l fr
equ
ency
mod
e
Tra
nsv
erse
fre
qu
ency
mo
de
Bla
des
fre
qu
ency
mod
e
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
124
Figure 6.8: A typical IAS response at EO10 for short blades; Case 1: Healthy blades
6.5 Results of the experiments on long blades
For the long blades, the extracted IAS signals were processed with different engine
order (EO) speeds to understand the appearance of blade resonance, their higher
harmonics and dynamic behaviour. It was observed that the EO5 and its higher
harmonics(EO10,EO15…)allcontained blade responses in the IAS signals. A hump
around 120 Hz to 130 Hz related to blade resonance (BR) indicates that the blades were
excited during the machine run-up and related in a banded resonance due to blade
mistuning. The figures are magnified around the BR region which shows the long
blade(s) frequency range from 115 Hz to 135 Hz so that the blade response in the
resonance region can be clearly seen. The EO5 responses in BR in the frequency band
between 115 Hz and 135 Hz for Cases 2 and 3 are shown in Figures 6.9 to 6.15 to
facilitate comparison with Case 1: healthy conditions. Similarly, Figures 6.16 to 6.22
are shown to compare the IAS response in the frequency band between 240 Hz and 270
Hz for faulty cases (Cases 2 and 3) with the healthy case (Case 1) at EO10. In addition,
Figures 6.23 to 6.29 are shown to compare the IAS response in the frequency band
between 345 Hz and 405 Hz for faulty cases (Cases 2 and 3) with the healthy case
(Case 1) at EO15. Figures 6.9 to 6.15 are combined in Figure C1 (Appendix C) showing
IAS data analysis for EO5 in blade health condition cases: healthy blades, blade root
looseness (Blade 1 or Blade 5), crack on one blade (Blade 2 or Blade 4) and cracks on
100 125 150 175 200 225 250 275 3000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
Short Blades
frequency mode
210-260 Hz Casing frequency
mode
Shaft 2nd
mode
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
125
two blades (Blades 2 and 4). In addition, Figures 6.16 to 6.22 are combined in
Figure C2 for EO10 and Figures 6.23 to Figure 6.29 are combined in Figure C3 for
EO15.
Figure 6.9: Measured encoder (IAS) data for engine order EO5 spectra for healthy blades
(Case 1)
Figure 6.10: Measured encoder (IAS) data for engine order EO5 spectra for blade root
looseness (Case 2(i): Blade no. 1)
115 120 125 130 1350
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ati
on
(R
PS
)
115 120 125 130 1350
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
126
Figure 6.11: Measured encoder (IAS) data for engine order EO5 spectra for blade root
looseness (Case 2(ii): Blade no. 5)
Figure 6.12: Measured encoder (IAS) data for engine order EO5 spectra for a crack on one
blade (Case 3(i): Blades no. 4)
115 120 125 130 1350
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ati
on
(R
PS
)
115 120 125 130 1350
0.05
0.1
0.15
0.2
0.25
0.3
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
127
Figure 6.13: Measured encoder (IAS) data for engine order EO5 spectra for cracks on two
blades (Case 3(ii): Blades no. 2 and 4)
Figure 6.14: Measured encoder (IAS) data for engine order EO5 spectra for a crack on one
blade (Case 3(iii): Blade 2)
115 120 125 130 1350
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Frequency (Hz)
EO
5, S
pee
d F
luct
ua
ted
(R
PS
)
115 120 125 130 1350
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ate
d (
RP
S)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
128
Figure 6.15: Measured encoder (IAS) data for engine order EO5 spectra for a crack on one
blade after 100 minutes of machine operation (Case 3(iv): Blade 4)
Figure 6.16: Measured encoder (IAS) data for engine order EO10 spectra for healthy blades
(Case 1)
115 120 125 130 1350
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ati
on
(R
PS
)
230 235 240 245 250 255 260 265 2700.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
129
Figure 6.17: Measured encoder (IAS) data for engine order EO10 spectra for blade root
looseness (Case 2(i): Blade no. 1)
Figure 6.18: Measured encoder (IAS) data for engine order EO10 spectra for blade root
looseness (Case 2(ii): Blade no. 5)
230 235 240 245 250 255 260 265 2700.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ati
on
(R
PS
)
230 235 240 245 250 255 260 265 2700.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
130
Figure 6.19: Measured encoder (IAS) data for engine order EO10 spectra for a crack on
one blade (Case 3(i): Blade no. 4)
Figure 6.20: Measured encoder (IAS) data for engine order EO10 spectra for cracks on two
blades (Case 3(ii): Blades no. 2 and 4)
230 235 240 245 250 255 260 265 2700
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ati
on
(R
PS
)
230 235 240 245 250 255 260 265 2700
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
131
Figure 6.21: Measured encoder (IAS) data for engine order EO10 spectra for a crack on one
blade (Case 3(iii): Blade no. 2)
Figure 6.22: Measured encoder (IAS) data for engine order EO10 spectra for a crack on one
blade after 100 minutes of machine operation (Case 3(iv): Blade no. 4)
230 235 240 245 250 255 260 265 2700
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
230 235 240 245 250 255 260 265 2700.02
0.04
0.06
0.08
0.1
0.12
0.14
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
132
Figure 6.23: Measured encoder (IAS) data for engine order EO15 spectra for healthy blades
(Case 1)
Figure 6.24: Measured encoder (IAS) data for engine order EO15 spectra for blade root
looseness (Case 2(i): Blade no. 1)
350 360 370 380 390 4000.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Frequency (Hz)
EO
15
, S
peed
Flu
ctu
ate
d (
RP
S)
350 360 370 380 390 4000
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Frequency (Hz)
EO
15
, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
133
Figure 6.25: Measured encoder (IAS) data for engine order EO15 spectra for blade root
looseness (Case 2(ii): Blade no. 5)
Figure 6.26: Measured encoder (IAS) data for engine order EO15 spectra for a crack on
one blade (Case 3(i): Blade no. 4)
350 360 370 380 390 4000.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Frequency (Hz)
EO
15
, S
peed
Flu
ctu
ati
on
(R
PS
)
350 360 370 380 390 4000.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Frequency (Hz)
EO
15
, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
134
Figure 6.27: Measured encoder (IAS) data for engine order EO15 spectra for cracks on two
blades (Case 3(ii): Blades no. 2 and 4)
Figure 6.28: Measured encoder (IAS) data for engine order EO15 spectra for a crack on one
blade (Case 3(iii): Blade no. 2)
350 360 370 380 390 4000.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
Frequency (Hz)
EO
15
, S
peed
Flu
ctu
ate
d (
RP
S)
(c)
350 360 370 380 390 4000.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
Frequency (Hz)
EO
15
, S
peed
Flu
ctu
ate
d (
RP
S)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
135
Figure 6.29: Measured encoder (IAS) data for engine order EO15 spectra for a crack on one
blade after 100 minutes of machine operation (Case 3(iv): Blade no. 4)
6.6 Results of the experiments on short blades
The vibration experiments were carried out for all the cases listed in Table 4.1 (i.e.
healthy blades, a crack on one blade and cracks on two blades). The same conditions
applied to long blades as described in Section 6.4 were also applied to short blades
during the experiments carried out for machine run-up. Also, here the extracted IAS
signals were processed with different EO speeds to understand the appearance of blade
resonance, their higher harmonics and dynamic behaviour. It was observed that EO10
spectra contained blade response in IAS signals. A hump around 210 Hz to 260 Hz
related to the BR region indicates that the blades were excited during the machine run-
up and related in a banded resonance due to blade mistuning. The magnified regions
covering BR related to blade frequencies are taken from IAS response signals and are
shown in Figures 6.30 to 6.32. The EO10 responses in BR in the frequency band
between 210 Hz and 260 Hz for Cases 3 are also shown in Figures 6.30 to 6.32 to
facilitate comparison with Case 1 of healthy conditions. Figures 6.30 to 6.32 are
combined in Figure C4 (Appendix C) showing IAS data analysis for EO10 in blade
health condition cases: healthy blades, crack on one blade and cracks on two blades.
350 360 370 380 390 4000
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
15
, S
peed
Flu
ctu
ati
on
(R
PS
)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
136
Figure 6.30: Measured encoder (IAS) data for engine order EO10 spectra for healthy blades
(Case 1)
Figure 6.31: Measured encoder (IAS) data for engine order EO10 spectra for a crack on one
blade (Case 3(i): Blade no. 5)
210 220 230 240 250 2600
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
210 220 230 240 250 2600
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
137
Figure 6.32: Measured encoder (IAS) data for engine order EO10 spectra for cracks on two
blades (Case 3(ii): Blades no. 5 and 7)
6.7 Observations and results
It is observed from the IAS response in the BR region in Figures 6.9 and 6.30 for long
and short blades, respectively, that the amplification of the BR region seems to be in a
banded form in Case 1: healthy blade conditions. The mistuned effect in both blades’
natural frequencies possibly resulted in the banded response. A typical EO5 and EO10
response in 1xBR region for the healthy blade case is shown in Figures 6.9 and 6.30 for
long and short blades, respectively. The presence of higher harmonics of BR for healthy
blades or higher harmonics of shaft speed for healthy machines is often observed in
experimental rigs and rotating machines. This is often due to a degree of misalignment
in the rotor at the coupling during machine assembly. However, it is important to
observe the dynamic behaviour and changes in the cases of faulty blade conditions.
Cases 2(i) and (ii) represent long blade root looseness at Blade no. 1 and Blade no. 5
separately as per Table 4.1. The addition of two loosely held washers at the root of
Blades no. 1 or 5 did not affect the natural frequency of the blades significantly; but the
210 220 230 240 250 2600
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
138
EO5, EO10 and EO15 responses of Blade no. 1 and Blade no. 5, shown in Figures 6.10
and 6.11 for EO5 and Figures 6.17 and 6.18 for EO10 and Figures 6.24 and 6.25 for
EO15, respectively, show significantly different behaviour compared to healthy
conditions (Case 1). It was observed that the 1xBR, 2xBR and 3xBR responses show
two or more distinct peaks instead of the single banded peak seen for Case 1: healthy
conditions.
Cases 3 represent tests related to cracked blades which consist of six tests, four tests
conducted on long blades and two tests on short blades as shown in Table 4.1. The tests
were related to either a single crack on one blade or two cracked blades together on the
same disc for either long or short blades. Distinct multiple peaks were observed in the
1xBR, 2xBR and 3xBR regions related to EO5, EO10 and EO15 for long blades,
respectively, and 1xBR region related to EO10 for short blades. These have been
observed for all crack related cases in Cases 3 for all six on long and short blades. This
distinct multiple peaking is evident from EO5 spectra in Figures 6.12 to 6.15, EO10
spectra in Figures 6.19 to 6.22 and EO15 spectra in Figures 6.26 to 6.29 for the four
crack related tests based on Cases 3 for long blades, and also in EO10 spectra in Figures
6.31 and 6.32 for the two tests on cracked short blades. It was observed that the 1xBR
plots show more distinct peaks instead of the single banded peak seen for Case 1 of
healthy blade condition for both blade types.
Crack breathing (opening and closing of cracks) that occurs with cracked blades and
interaction due to blade root looseness seem to be responsible for the multiple peaks in
the BR region and its higher harmonics. From the results of the three cases, it can be
observed that shaft torsional vibration based on IAS signals and the appearance of
multiple peaks during machine run-up may be useful for blade health monitoring
(BHM).
6.8 Summary
The measured shaft torsional vibration based-on IAS signal based on encoder data for
long and short blades during machine run-up operation was used to understand the
dynamics of the rotating blades with and without faults and to compare the behaviour of
long and short blades under different health conditions. Experiments were conducted for
CHAPTER 6 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
139
three different blade health conditions; healthy blades with mistuned effects, blade root
looseness and cracked blades, in the case of long blades, and for two conditions; healthy
with mistuned effects and cracked blades, in the case of short blades. The IAS signals
for blades when order tracked with EO5 and its higher harmonics (EO10 and EO15) for
long blades and EO10 for short blades show the existence of blade resonance (BR) for
all three cases. It was observed that the single banded peak for healthy blades (Case 1)
in the 1xBR, 2xBR and 3xBR changes to multiple peaks in 1xBR, 2xBR and 3xBR for
damaged conditions in the cases of both long and short blades. The results show a
distinct difference between healthy and faulty blade conditions and hence these
observations may be useful for BHM.
CHAPTER 7 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
140
CHAPTER 7
COMPARATIVE STUDY BETWEEN OBV, OCV AND IAS METHODS
7.1 Introduction
This chapter presents a comparison between the three measurements: on-bearing
vibration (OBV), on-casing vibration (OCV) and shaft torsional vibration based-on
instantaneous angular speed (IAS), discussed in Chapters 4, 5 and 6, respectively. The
measurements are used in the experimental work of this research study in order to
investigate blades’ dynamic behaviour during machine run-up under three different
blade health conditions; (a) healthy blades with mistuned effects, (b) blade root
looseness, and (c) cracked blades, for two blade sizes (long and short). The purpose of
this study is to investigate which of these measurements represents a more useful and
reliable method to use for diagnosis and detection of blade health conditions. The full
details of the three measurements, including blade/rig design and blade faults simulation
are discussed in Chapters 3, 4, 5 and 6. Figure 7.1 shows a schematic diagram of the
procedures followed for the selection of the most suitable method with the best
measurement that can be used as a tool for blade health monitoring (BHM) of rotating
machines. Table 7.1 and Table 7.2 show a comparison of the results of the
measurements for short and long blades, respectively.
Figure 7.1: A schematic flowchart for the processes that are followed to select the best
measurement for BHM of rotating machines
ROTATING MACHINES BLADES
VIBRATION MEASUREMENT
TECHNIQUES FOR BHM
OBV OCV
IAS
Investigate Measurements
Best Method could be selected as a
diagnoses tool
CHAPTER 7 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
141
Table 7-1 : Comparison between the results of OBV, OCV and IAS for short blades
Blade
condition
Short blades
OBV OCV IAS
Case 1:
Healthy
with
mistuned
effects
There are no responses in the blade resonance
(BR) regions (1xBR).
There is one distinct peak around the
frequency 238 Hz confirming excitation of
blade resonance (1xBR).
Existence of BR in the 1xBR region
indicates blades are excited during the
machine run-up and related in a banded
resonance due to the blade mistuning; one
banded peak was observed.
220 225 230 235 240 245 250 255 2600.016
0.018
0.02
0.022
0.024
0.026
0.028
0.03
0.032
0.034
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
220 230 240 250 2600
0.005
0.01
0.015
0.02
0.025
0.03
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
210 220 230 240 250 2600
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
CHAPTER 7 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
142
Case 3(i):
Crack on
one blade
No BR and no changes in the harmonics region
of (1xBR).
There are no changes in the blade behaviour
related to EO10 in (1xBR) region compared to
Case 1: healthy conditions; only a single
distinct peak was observed.
The existence of BR in 1xBR region was
observed and 1xBR shows more distinct
peaks instead of a single banded peak for
Case 1, healthy conditions.
Case 3(ii):
Cracks on
two blades
There are distinct multiple peaks in the 1xBR
regions related to the EO10 response.
As in Case 3(i); no changes were observed in
the blade harmonics region of (1xBR)
compared to Case 1, healthy conditions.
The existence of BR in 1xBR region.
Again, more distinct peaks in 1xBR
region were observed instead of a single
banded peak for Case 1, healthy
conditions.
220 225 230 235 240 245 250 255 2600.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.022
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
220 225 230 235 240 245 250 255 2600
0.005
0.01
0.015
0.02
0.025
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
210 220 230 240 250 2600
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
220 225 230 235 240 245 250 255 2600.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
220 225 230 235 240 245 250 255 2600
0.005
0.01
0.015
0.02
0.025
0.03
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
210 220 230 240 250 2600
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
10
, S
peed
Flu
ctu
ate
d (
RP
S)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
Table 7-2: Comparison between the results of OBV and IAS for long blades
Blade
condition
Long Blades
OBV IAS
Case 1:
Healthy
with
mistuned
effect
There is one peak around 122 Hz,
which represents BR response in
(1xBR) region.
Existence of BR in (1xBR) region
indicates the blades are excited during
the machine run-up and related in a
banded resonance due to the blade
mistuning; one banded peak was
observed.
Case 2:
Blade root
looseness
There are distinct multiple peaks in
the 1xBR regions related to the EO5
response.
The existence of BR in 1xBR region;
more distinct peaks were observed in
the 1xBR region instead of the single
banded peak seen for Case 1, healthy
condition.
Case 3(i):
Crack on
one blade
There are distinct multiple peaks in The existence of BR in 1xBR region. It
115 120 125 130 135
0
0.5
1
1.5
2
x 10
-7
EO5 Frequency (Hz)
Ac
ce
le
ra
tio
n (m
/s
2
)
(a)
115 120 125 130 1350
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ati
on
(R
PS
)
(a)
115 120 125 130 1351
1.5
2
2.5
3
3.5
4
4.5
5
5.5x 10
-4
EO5 Frequency (Hz)
Accele
ra
tio
n (
m/s
2)
(c)
115 120 125 130 135
0
0.05
0.1
0.15
0.2
E05 Frequency (Hz)
Sp
ee
d F
lu
ctu
atio
n (R
PS
)
(d)
115 120 125 130 1350.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2x 10
-4
Frequency (Hz)
EO
5, A
ccele
ra
tio
n (
m/s
2)
115 120 125 130 1350
0.05
0.1
0.15
0.2
0.25
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ate
d (
RP
S)
(b)
CHAPTER 7 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
144
the 1xBR regions related to the EO5
response.
was observed that the 1xBR shows
more distinct peaks instead of the
single banded peak seen for Case 1,
healthy condition.
Case 3(ii):
Cracks on
two blades
There are distinct multiple peaks in
the (1xBR) regions related to the
EO5 response.
The existence of BR in 1xBR region.
More distinct peaks were observed in
the 1xBR region instead of the single
banded peak for Case 1, healthy
condition.
7.2 Results and observations
7.2.1 Comparison of the three measurements for monitoring short blades
TheresultsofthethreemeasurementsOBV,OCVandIASarecomparedinTable7.1.
The threemeasurementswere used to detect and diagnose blade health conditions in
differentcasesofblade faults inorder to selectwhichof these threemeasurements is
moreeffectiveandreliabletobeusedforBHM.Theobservationsshowthat:
1- TheresultsobtainedfromOBVsignalsprovidedsomegoodindicationforblade
faultdetectiononly in thecasesofmore thanonecrackedbladeon thebladed
disc,asshowninCase3(ii)inTable7.1.Thiswasdetectedbytheexistenceof
distinct multiple peaks in the BR region of 1xBR related to the EO10 response.
2- TheresultsobtainedfromOCVsignalsshowedthattherewereno changes in the
BR region for damaged blades in Cases 3(i) and 3(ii) compared with Case 1 of
healthy conditions, as shown in Table 7.1. Only one distinct peak was observed
for all three cases.
3- The results obtained based on IAS signals provided a good indication for the
detectionofblade fault conditions;onebandedpeakwasobserved forhealthy
115 120 125 130 1350
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
-4
Frequency ( Hz)
EO
5, A
ccele
ra
tio
n (
m/s
2)
115 120 125 130 1350
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Frequency (Hz)
EO
5, S
peed
Flu
ctu
ate
d (
RP
S)
(c)
CHAPTER 7 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
145
blades inCase1 inTable7.1 in thebladeresonanceregion,whichchanged to
morepeaksintheBRregionfordamagedconditionsinCases3(i)and3(ii).
7.2.2 Comparison of the two measurements for monitoring long blades
The comparison results are provided in Table 7.2, which includes a full comparison
between the twomeasurements,OBV and IAS, applied in four cases of blade health
conditions.Theobservationsshowthat:
1- The results extracted from OBV signals provided a good indication for the
detection of blade fault conditions: the presence of distinct multiple peaks in the
BR region for damaged blades in Cases 2 and 3 instead of one peak in the BR as
in Case 1, healthy conditions as shown in Table 7.2.
The results of IAS signals gave a good indication for the detection of blade fault
conditions; a single banded peak for Case 1, healthy blade conditions, in BR region
changestomorepeaksintheBRregionfordamagedbladeconditionsasseeninCases
2and3,asshowninTable7.2.
7.2.3 Final conclusion
TheresultsshowninTables7.1and7.2,includingthefiguresofengineorders(EOs)of
the1xBRregion for shortand longbladesduringmachine run-updescribe theuseof
OBV,OCVandIASmeasurementsinordertoinvestigate which measurementare more
useful for BHM. The results indicate that:
- OCVisnotusefulasafeatureinBHMbasedontheresultsofthisstudy.Further
experimentsinfuturestudiesarerecommended.
- OBVprovideda good indication to detect faults in long blades. However, the
results related to short blades also provided some good indication only in the
caseswhentherewasmorethanonecrackedbladeonthebladeddisc.Hence the
presence of distinct multiple peaks in the BR region can be considered as a
feature of BHM.
- IASprovidedagoodindicationtomonitorbladehealthconditionsforbothshort
and longblades.Thepresenceofdistinctmultiplepeaks in theBR regioncan
thereforebeconsideredasahighlyusefulfeatureofBHM.Indeed,theshaftIAS
CHAPTER 7 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
146
signalseemstogiveamuchbetterindicationforBHMcomparedtoOBVand
OCVmeasurements.
In addition to the above, Table 7.3 illustrates a summarised comparison between the
threemeasurement techniques inpublishedwork.Theserepresent the resultsobtained
from thesemeasurements which have been published in international journals or the
proceedingsofconferences.
Table 7-3: Articles published related to this study in the research area of blade vibration fault
detection using different measurements
Blade
condition
Shortblades Longblades
OBV OCV IAS OBV IAS
ArticlePublished
Healthy
VETOMAC-X
2014[127]
VETOMAC-X
2014[127]
ASME
2014[128]
SOP 2014
[129]
ICOVP 2013
[130]
MSSP 2014
[131]
ASME 2014
[128]
SOP 2014
[129]
ICOVP 2013
[130]
1Crack
2Cracks
Root
looseness
--------
--------
--------
7.3 Summary
A comparison between the results of the three measurements of OBV, OCV and IAS
was discussed in this chapter. These measurements were used to investigate blades’
dynamic behaviour under different types of blade fault conditions in order to choose
which measurement is the most suitable to be used as a tool for BHM. From the results
listed in Tables 7.1 and 7.2 for both short and long blades, the shaftIASmeasurement
seemstoprovidethebestindicationforthedetectionofbladefaultscomparedtoOBV
and OCV measurements.Hence,shaftIASvibrationcouldbeconsideredasafeatureof
BHM.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
CHAPTER 8
DEVELOPMENT OF POLAR PLOT PRESENTATION (P3)
METHOD
8.1 Introduction
In this chapter, a novel approach was used for presenting the vibration data by using
polar plot coordinates relating to blade order angles and the vibration amplitude of on-
bearing vibration (OBV), on-casing vibration (OCV) and shaft torsional vibration based
on instantaneous angular speed (IAS) signals. The polar plot presentation (P3) is a two
dimensional coordinate system in which each point in the graph is determined by an
angle and a distance. The polar presentation is used to detect blade faults in rotating
machines and to determine the feasibility of using the polar plot method for blade health
monitoring (BHM). In this research, a P3 presentation method is applied by
representing the summation of time synchronizing averaging (TSA) data of vibration
measurementsrelatedtodifferentengineordersofblade(s)resonance(BR)frequency.
The P3 method was used for analysis of experimental vibration data of a rotating 8-
bladed discs during machine run-up in order to detect blade faults using three cases of
blade health conditions: (a) healthy with mistuned effects, (b) blade root looseness and
(c) crack(s) on blade(s).
8.2 Signal processing concept developed
Signal processing developed for the P3 method is discussed in the following sections.
8.2.1 Filter for each blade resonance related to the engine orders
In order to determine the BR related to the engine order (EO) of first resonance
frequency of blades (1xBR), a band-pass filter was used for the data analysis using
MATLAB to complete this task. 1xBR was firstly determined from the blade natural
frequency, which was obtained through modal testing using a frequency response
function (FRF) method, as described in Section 3.6. The EOs were then computed in
relation to the BR frequency. The band-pass filter was then used to determine blades
higher harmonics region (2xBR,3xBR…).TheP3processwassubsequentlyappliedin
relation to the BR region for accurate data analysis.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
148
8.2.2 Combining engine orders
In addition to the use of a single EO, the combination of different engine orders related
to BR frequency when using the P3 method also provided good results. The
combinations of EOs was done by summation of a random selection of two, three or
four of different EOs related to different BR frequency harmonics (1xBR, 2xBR, 3xBR
and 4xBR), i.e, for long blades a combination of EO5+EO10+EO15 was selected,
which is related to BR frequencies 1xBR, 2xBR and 3xBR respectively. This can be
used to determine which combination of EOs gives a clear result when comparing data
for faulty blades with healthy blades for the purpose of diagnosing blade conditions.
The use of combinations of different EOs provides a wide range of results from
analysed data leading to a more reliable P3 method leading to more effective BHM.
8.2.3 Time synchronous averaging for each engine order related to blade
resonance
TSA is a signal processing method which is used to extract waveform signals from
other noisy data. TSA was used in this study for the P3 method to enhance the time
domain data after application of the band-pass filter to remove the noise from the
vibration signals. It is a very powerful tool, used to eliminate signal noise components
that are not synchronous with the shaft torsional rotation, such as noise generated by the
electrical motor, the bearing, and any vibration not related to the rotating shaft. This is
effective when used to separate blades’ response via shaft torsional vibration that is
coupled with its vibration from other noisy signals. To achieve this, tachometer signals
are needed as a reference (for shaft rotation), together with the angular position of the
rotating shaft with blades. On the other hand, the TSA is also essential, which will
repeat the vibration signals related to the blades on the rotating shaft periodically, by
dividing the vibration signal into adjacent segment windows with the same length, then
sampling the segments until the shaft rotation is enhanced and other parts of the signals
can be removed. The TSA waveform is calculated based on the shaft torsional vibration
(IAS), OBV and OCV, together with the tachometer signals. The TSA corresponding to
shaft rotation of one full revolution can be obtained from the tachometer signal. The fast
Fourier transformation (FFT) is then calculated on the angular domain instead of the
time domain signal for every segment after resampling the time domain data. Finally,
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
149
the polar plot presentation is applied to the FFT data to discover the blade vibration
behaviour for healthy and faulty cases, which are then compared.
8.3 Polar plot
The P3 method is a novel approachusedforpresentingtheblades’vibrationbehaviour
using the polar coordinates (angle and amplitude) of engine orders related to blade order
angles and the vibration amplitude of three measurements: shaft torsional vibration, on-
bearing vibration and on-casing vibration. The polar plot is a two dimensional
coordinate system in which each point in the graph is determined by an angle and a
distance. The radius of the system corresponds to the blade vibration signal amplitude
and the 360 angular coordinate is used to represent the 360 degrees of the full rotation
of the shaft. The novelty of this method is in its application to the detection of faulty
blades. This is done by representing the summation of TSA data of blade vibration
relatedtobladesEOsusingthefollowingsteps:applyingaband-passfilteraroundthe
frequency region of the first blade resonance (1xBR), followed by all its higher
harmonics(2xBR,3xBRand4xBR)usingIAS,OBVandOCVdataextractedfromthe
encoder,thebearingpedestalsandthecasingrespectively.Thisprocedureisappliedto
remove other effect signals not related to BR. Next, BR-related signals are added
togetherwith reference to tachometer signalsusing theTSAmethod.Finally, thedata
areplottedin2Dpolarcoordinates.
8.3.1 Polar plot diagnosis approach
P3 is a novel method used in this study to diagnose and detect blade health conditions in
rotating machines. The radius in the plot represents the amplitude of the signal, and the
anglerepresents therotor’sfullrotation cycle of 360 degrees, as shown in Figure 8.1.
P3 is related to a rotating 8-blade disc spaced by 45 angles, and the amplitude of the
vibration signals of single EO and/or combination of EOs (IAS, OBV and OCV) is
related to blade resonance frequency (1xPR, 2xPR, 3xPR and 4xPR). By comparing the
P3plotsofhealthywithfaultyblades’conditions,bladelooseness,acrackononeblade
and cracks on two blades can be detected, and this can be utilized as an effective tool for
BHM.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
150
Figure 8.1: Typical sample of the polar plot presentation model
The P3 approach works by computing the number of leaves (L) and the number of
coupledleaves(C)andcomparingthechangesintheleaves’profileshapeforthetwo
cases of blade health conditions (healthy versus faulty) to help diagnose blade
conditions. The typical P3 model shown in Figure 8.1 can be divided leaves shape into
three types: (L) represents a single leaf, (C2) represents two leaves coupled together
which are joint at point (a1), and (C3) denotes three leaves coupled together, joint at two
points (b1) and (b2). In the same way, different numbers of coupled leaves can be
writtentogetherasC4,C5,andC6…etc.Inthecasewheremorethanoneofeachtype
is seen, this can be written as a set, such as 5L, 3C2 or 2C3 etc. In the case presented in
Figure 8.1, the set of eleven single and coupled leaves can be counted as: two single
leaves (2L), three of two coupled leaves (3C2) and one of three coupled leaves (1C3),
and therefore the whole set can be written as 11L = 2L+3C2+1C3.
The P3 method analysis processes are followed in order to discover and detect blade
faults and are carried out by comparing the differences in polar plot shapes between
a1
b2
b1
3 coupled
leaves (C3)
Single leaf (1L)
2 coupled
leaves (C2)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
151
cases of healthy and faulty blade conditions. The plots can be compared and monitored
based on the following parameters:
(a) The total number of leaves in the plot.
(b) The number and nature of the coupled leaves.
(c) The deviation of the leaves from the centre of the plot.
(d) The symmetry and/or regularity of the leaves in the plot.
(e) The full profile and the size of the single leaves and/or combined leaves.
(f) The amplitude of the blade frequency in each case.
8.4 Polar plot presentation (P3) for long blades
For long blades, the region of blades natural frequency is determined at different EOs
using a band-pass filter based on MATLAB software. This is determined for long
blades at EO5 in the range between 115 Hz and 135 Hz and then the process proceeds to
its higher harmonics(EO10,EO15,EO20…).Thepolarplotisthenappliedusingpolar
coordinates of angle and signal amplitude at one mean or 2 means of speed engine order
(EO5)relatedtothelongblades’firstresonancefrequency(1xBR)toavoidanynoiseat
the root of the signal, which may affect the quality of analysis, as illustrated in Figure
8.2. By applying polar plots, blades’ behaviour can be compared at different blade
health cases of: (a) healthy blades with mistuned effects, (b) blade root looseness, (c) a
crack on one blade, and (d) cracks on two blades. The data can be extracted from the
rotating blades during machine run-up by using an encoder for torsional vibration based
on IAS signal and accelerometers for acceleration vibration signals from the bearing
pedestals and from the casing.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
152
Figure 8.2: Mean speed of EO5 for IAS data from healthy long blades
8.4.1 Encoder data analysis
The polar plot presentation method was conducted for the encoder torsional vibration
based on IAS signals in order to compare blades dynamic behaviour in four cases of
blade health conditions; (a) healthy with mistuned effects, (b) blade root looseness, (c) a
crack on one blade, and (d) cracks on two blades, at different EOs with and without
combination of EOs as shown in Figures 8.3 to 8.7 and Tables 8.1 to 8.5.
90 100 110 120 130 140 1500
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
X: 117.1
Y: 0.0479
Frequency (Hz)
EO
5, S
peed
(m
/s)
X: 135.9
Y: 0.04793
Blades resonance frequencey (BR)
Mean of EO5 (Y=0.0479)
Polar plot apply at this red line
(RP
S)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
153
Figure 8.3: Polar plot of IAS measured data of EO5 for cases: (a) healthy blades, (b) blade root
looseness, (c) a crack on one blade and (d) cracks on two blades
Table 8-1: The numbers of single leaves and coupled leaves in the polar plot of EO5 for the
four cases of blade health conditions based on IAS data
Case Healthy blades
(a)
Blade looseness
(b)
Crack on one blade
(c)
Cracks on two
blades (d)
EO5 5L
(5L)
5L
(1L+1C4)
5L
(1L+1C4)
5L
(1C5)
0.005
0.01
0.015
0.02
0.025
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
30
210
60
240
90
270
120
300
150
330
180 0
(d) (c)
(a) (b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
154
Figure 8.4: Polar plot of IAS measured data of EO10 for cases: (a) healthy blades, (b) blade
root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-2: The numbers of single leaves and coupled leaves in the polar plot of EO10 for the
four cases of blades health conditions based on IAS data
Case Healthy blades
(a)
Blade looseness
(b)
Crack on one blade
(c)
Cracks on two blades
(d)
EO10 10L
(4L+1C6)
9L
(4L+1C5)
9L
(3L+1C6)
9L
(3L+1C6)
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
0.1
0.2
0.3
0.4
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
0.08
0.1
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(d) (c)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
155
Figure 8.5: Polar plot of IAS measured data of EO15 for cases: (a) healthy blades, (b) blade
root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-3: The numbers of single leaves and coupled leaves in the polar plot of EO15 for the
four cases of blades health conditions based on IAS data
Case Healthy blades
(a)
Blade looseness
(b)
Crack on one blade
(c)
Cracks on two blades
(d)
EO15 14L
(14L)
15L
(2L+1C13)
15L
(1L+1C14)
14L
(3L+1C11)
0.05
0.1
0.15
0.2
0.25
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(d) (c)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
156
Figure 8.6: Polar plot of IAS measured data of EO5+EO10 for cases: (a) healthy blades, (b)
blade root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-4: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO5+EO10 for the four cases of blades health conditions based on IAS data
Case Healthy blades
(a)
Blade looseness
(b)
Crack on one
blade (c)
Cracks on two
blades (d)
EO5+EO10 10L
(4L+1C6)
9L
(1C2+1C7)
10L
(3C2+1C4)
9L
(3L+1C6)
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
0.2
0.4
0.6
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
0.08
0.1
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (d)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
157
Figure 8.7: Polar plot of IAS measured data of EO5+EO10+EO15 for cases: (a) healthy blades,
(b) blade root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-5: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO5+EO10+EO15 for the four cases of blades health conditions using IAS data
Case Healthy blades
(a)
Blade looseness
(b)
Crack on
one blade (c)
Cracks on two blades
(d)
EO5+EO10+EO15 14L
(2C3+2C4)
11L
(1L+2C2+2C3)
13L
(1C3+1C10)
11L
(1L+1C10)
0.1
0.2
0.3
0.4
30
210
60
240
90
270
120
300
150
330
180 0
0.2
0.4
0.6
0.8
30
210
60
240
90
270
120
300
150
330
180 0
0.1
0.2
0.3
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (d)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
158
8.4.2 Encoder data observations and discussion
It is observed from the polar plot presentation for encoder IAS response of blades’
behaviour in Figures 8.3 to 8.7 for the four cases of blade health conditions that the
number of leaves are the same for the four cases of blade condition of EO5, however the
number of combined leaves and the profile are different in faulty blade cases (b), (c) and
(d) compared to the healthy blades case (a), as shown in Figure 8.3 and Table 8.1. For
EO10, it can be clearly seen that the number of leaves changed from 10 in the healthy
case (a) to 9 leaves in the three faulty cases (b), (c) and (d), as shown in Figure 8.4 and
Table 8.2. In Figure 8.5 for the data of EO15, there are 14 full regular separate leaves in
case (a) of healthy blades, which changed to 15 irregular leaves, some of which were
combined, in faulty blade cases (b) and (c), and 14 leaves in case (d), also with irregular
shapes and featuring some combined leaves. Also, the same result was observed in the
combination between different engine orders, (EO5+EO10) and (EO5+EO10+EO15), as
shown in Figures 8.6 and 8.7, respectively. These show that the number of leaves
changed from 10 for the healthy case (a) to 9 leaves for the three faulty cases (b), (c)
and (d), with different sets of combined leaves as shown in Figure 8.6 and Table 8.4.
Likewise, Figure 8.7 and Table 8.5 show a change in the number of leaves from 14 for
the healthy blades case (a) to 11 and 13 for faulty blade cases, with different sets of
combined leaves for the combination of three EO’s (EO5+EO10+EO15). Hence, from
the results of the four cases of blade health conditions, it can be observed that the polar
plot presentation method can be useful to analyse shaft torsional vibration data based on
IAS signals in order to diagnose blade health conditions under different blade faults, as
the changes in the shape and number of separate and combined leaves and the number
of leaves in each coupling during machine run-up can be indicative of blade health
conditions.
8.4.3 On-bearing data analysis
The polar plot presentation method was also used to analyse data of blade(s) vibration
response obtained from the OBV, using an accelerometer in order to compare the long
blades’dynamicbehaviour in fourcasesofbladehealthconditions, as listed in Table
4.1: (a) healthy with mistuned effect, (b) blade root looseness, (c) a crack on one blade,
and (d) cracks on two blades using different Eos and a using combination of several
engine orders, as shown in Figures 8.8 to 8.11.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
159
Figure 8.8: Polar plot of acceleration (OBV) measured data of EO15 for cases: (a) healthy
blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-6: The numbers of single leaves and coupled leaves in the polar plot of EO15 for the
four cases of blade health conditions using OBV data
Case Healthy
blades (a)
Blade looseness
(b)
Crack on one
blade (c)
Cracks on two
blades (d)
EO15 13L
(3L+1C13)
15L
(9L+1C6)
16L
(3L+1C13)
16L
(4L+1C12)
0.0002
0.0004
0.0006
30
210
60
240
90
270
120
300
150
330
180 0
0.0002
0.0004
0.0006
30
210
60
240
90
270
120
300
150
330
180 0
0.0001
0.0002
0.0003
0.0004
0.0005
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (d)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 8.9: Polar plot of acceleration (OBV) measured data of EO5+EO10 for cases: (a)
healthy blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-7: The numbers of single leaves and coupled leaves in the polar plot of a combination
of EO5+EO10 for the four cases of blade health conditions using OBV data
Case Healthy
blades (a)
Blade looseness
(b)
Crack on one
blade (c)
Cracks on two
blades (d)
EO5+EO10 9L
(1C2+1C7)
10L
(5C2)
15L
(1C3+1C12)
10L
(5C2)
Figure 8.9: Polar plot of acceleration (OBV) measured data of EO5+EO10 for cases: (a)
0.0002
0.0004
0.0006
0.0008
0.001
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
0.002
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
0.002
0.0025
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (d)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 8.10: Polar plot of acceleration (OBV) measured data of EO5+EO10+EO15 for cases:
(a) healthy blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-8: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO5+EO10+EO15 for the four cases of blade health conditions using OBV data
Case Healthy
blades (a)
Blade looseness
(b)
Crack on one blade
(c)
Cracks on two
blades (d)
EO5+EO10 12L
(1C12)
14L
(1C2+4C3)
14L
(1L+1C2+2C3+1C5)
13L
(2C2+3C3)
0.0005
0.001
0.0015
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
30
210
60
240
90
270
120
300
150
330
180 0
0.0002
0.0004
0.0006
0.0008
0.001
30
210
60
240
90
270
120
300
150
330
180 0
0.001
0.002
0.003
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (d)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 8.11: Polar plot of acceleration (OBV) measured data of EO10+EO20 for cases: (a)
healthy blades, (b) blade root looseness, (c) crack on one blade and (d) cracks on two blades
Table 8-9: The numbers of single leaves and coupled leaves in the polar plot of combination of
EO10+EO20 for the four cases of blade health conditions using OBV data
Case Healthy
blades (a)
Blade looseness
(b)
Crack on one
blade (c)
Cracks on two
blades (d)
EO10+EO20 15L
(3C2+1C9)
20L
(6C2+1C8)
20L
(6C2+1C3+1C5)
20L
(3C3+1C14)
0.0005
0.001
0.0015
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
30
210
60
240
90
270
120
300
150
330
180 0
0.0005
0.001
0.0015
0.002
0.0025
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (d)
(b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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8.4.4 On-bearing data observations and discussion
The polar plot presentation method for long blades’ acceleration response behaviour
during machine run-up is shown in Figures 8.8 to 8.11 for the four cases of blade health
conditions. The blade response using the P3 method for EO15 shows the number of
leaves changed from 13 leaves for the healthy case (a) to 15 leaves for the case of blade
root looseness (b), and to 16 leaves in the cases of cracked blades (c) and (d), with
different numbers of coupled leaves for all four cases, as shown in Figure 8.8 and Table
8.6. Upon combination between different EOs (EO5+EO10), (EO5+EO10+EO15) and
(EO10+EO20), as shown in Figures 8.9-8.11 and the related Tables 8.7-8.9 respectively,
it can be observed that the number of leaves increased in all three cases of faulty blade
conditions (cases (b), (c) and (d)) compared to healthy conditions (case (a)), and the
shapes of separate leaves and coupled leaves, along with the number of coupled leaves
in the profiles, are different compared to the case of healthy blade conditions. Hence,
from the results of the four cases of blade health conditions, it can be observed that the
P3 method gave a good indication for the diagnosis of blade health conditions and
provided a useful visual means when used to analyse on-bearing vibration data response
to discover different blade faults. The visual features include changes in the shape and
number of separate leaves, changes in the shape and number of coupled leaves, and the
number of leaves in each coupling during machine run-up.
8.5 Polar plot presentation (P3) for short blades
The region of blades natural frequency for short blades was determined at different EOs
using a band-pass filter using MATLAB software. This region was found for short
blades at EO10 to be within the range between 220 Hz and 260 Hz, and in the same
way, its higher harmonics (EO20, EO30, EO40…) were also determined. Polar plot
was applied using polar coordinates, angle and signal amplitude, at 2 means from the
top peak of the engine order signal related to first blade resonance frequency (1xBR), in
order to avoid any noise at the root of the signal and to improve the accuracy of the
results. Subsequently, FFT was computed at different engine orders and TSA was
performed for each signal sample in the time domain when the tachometer signal was
triggered, to avoid all non-synchronous parts of the signal in the spectrum. Finally, the
polar plot was applied to the data using single EO and combinations of different EOs to
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
164
compare the blades’ behaviour in different blade health cases of: (a) healthy blades, (b)
a crack on one blade, and (c) cracks on two blades. The data were extracted from the
rotating blades during machine run-up by using the data of the encoder for torsional
vibration based on IAS signals and accelerometers for OBV and OCV.
8.5.1 Encoder data
The polar plot presentation method was conducted on encoder torsional vibration based
on IAS signals in order to compare the blades’ dynamic behaviour in three cases of
blade health conditions: (a) healthy, (b) a crack on one blade, and (c) cracks on two
blades, using different EOs and combinations of EOs, as shown in Figures 8.12-8.15.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 8.12: Polar plot of IAS measured data of EO10 for cases: (a) healthy blades, (b) crack
on one blade and (c) cracks on two blades
Table 8-10: The numbers of single leaves and coupled leaves in the polar plot of EO10 for the
three cases of blade health conditions using IAS data
Case Healthy
blades (a)
Crack on one
blade (b)
Cracks on two
blades (c)
EO10 10L
(10L)
10L
(2L+1C8)
10L
(1L+1C9)
0.05
0.1
0.15
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
0.08
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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Figure 8.13: Polar plot of IAS measured data of EO20 for cases: (a) healthy blades, (b) crack
on one blade and (c) cracks on two blades
Table 8-11: The numbers of single leaves and coupled leaves in the polar plot of EO20 for the
three cases of blade health conditions using IAS data
Case Healthy
blades (a)
Crack on one
blade (b)
Cracks on two
blades (c)
EO20 21L
(7L+1C14)
20L
(7L+1C5+1C8)
19L
(2L+1C17)
0.05
0.1
0.15
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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Figure 8.14: Polar plot of IAS measured data of EO10 + EO20 for cases: (a) healthy blades, (b)
crack on one blade and (c) cracks on two blades
Table 8-12: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+E20 for the three cases of blade health conditions using IAS data
Case Healthy
blades (a)
Crack on one
blade (b)
Cracks on two
blades (c)
EO10+EO20 19L
(4C2+1C11)
20L
(1C20)
20L
(1C2+1C18)
0.05
0.1
0.15
0.2
0.25
30
210
60
240
90
270
120
300
150
330
180 0
0.1
0.2
0.3
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
0.2
0.25
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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Figure 8.15: Polar plot of IAS measured data of EO10+EO30 for cases: (a) healthy blades, (b)
crack on one blade and (c) cracks on two blades
Table 8-13: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+E30 for the three cases of blade health conditions using IAS data
Case Healthy
blades (a)
Crack on one
blade (b)
Cracks on two
blades (c)
EO10+EO30 27L
(3C3+1C18)
29L
(1C3+1C26)
30L
(1L+1C9+1C20)
0.05
0.1
0.15
0.2
0.25
30
210
60
240
90
270
120
300
150
330
180 0
0.1
0.2
0.3
0.4
30
210
60
240
90
270
120
300
150
330
180 0
0.05
0.1
0.15
0.2
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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8.5.2 Encoder data observations and discussion
It is observed from the P3 presentation for the encoder data of shaft torsional vibration
based on IAS response signals for short blades, as shown in Figures 8.12-8.15 for three
cases of blade health conditions, that the number of leaves is the same for the three
cases of blade conditions at EO10, however the shape and number of combined leaves
are different in faulty blade cases (b) and (c) compared to the healthy blade case (a),
which featured very regular shapes as shown in Figure 8.12 and Table 8.10. Also, the
secondblades’ resonance frequency atEO20 clearly shows that the number of leaves
changed from 21 with uniform shapes of leaves for the healthy case (a) to 20 and 19
leaves with irregular shapes for the faulty cases (b) and (c) respectively, as shown in
Figure 8.13 and Table 8.11. Upon combination between different EOs of (EO10+EO20)
and (EO10+EO30), as shown in Figures 8.14 and 8.15 respectively, it was observed that
the number of leaves in faulty blade conditions, cases (b) and (c), was higher compared
to the healthy case (a) for both combinations. In addition, the shape of the separate
leaves and coupled leaves and the number of coupled leaves changed compared to the
healthy blade case (a). Hence, from the results of the three cases of blade health
conditions, it can be observed that the P3 method is effective in detecting and
discovering blade health conditions under different blade faults when used to analyse
shaft torsional vibration based on IAS signals, and the changes in the numbers and
shapes of separate and coupled leaves, in addition to the number of leaves in each
coupling, can provide a good indication of blade health, and therefore this tool could be
useful in BHM.
8.5.3 On-bearing data analysis
The polar plot presentation method was used for analysis of short blades’ vibration
responses during machine run-up, which was obtained from the bearing pedestal using
an accelerometer in order to describe and compare the blades’ dynamic behaviour in
three cases of blade health conditions; (a) healthy, (b) a crack on one blade, and (c)
cracks on two blades, at different combinations of EOs, as shown in Figures 8.16-8.19.
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Figure 8.16: Polar plot of acceleration (OBV) measured data of EO10+EO20 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-14: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+E20 for the three cases of blade health conditions using OBV data
Case Healthy
blades (a)
Crack on one
blade (b)
Cracks on two
blades (c)
EO10+EO20 20L
(10C2)
20L
(1C20)
20L
(1C20)
0.02
0.04
0.06
0.08
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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Figure 8.17: Polar plot of acceleration (OBV) measured data of EO10+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-15: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+E40 for the three cases of blade health conditions using OBV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO10+EO40 35L
(3C4+1C23)
40L
(1L+1C39)
40L
(3L+1C3+1C34)
0.02
0.04
0.06
0.08
0.1
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
0.08
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
0.05
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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Figure 8.18: Polar plot of acceleration (OBV) measured data of EO20+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-16: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO20+E40 for the three cases of blade health conditions using OBV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO20+EO40 35L
(5L+15C2)
38L
(5C2+1C28)
40L
(6L+1C34)
0.02
0.04
0.06
0.08
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
0.05
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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Figure 8.19: Polar plot of acceleration (OBV) measured data of EO10+EO20+EO30 for cases:
(a) healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-17: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+EO20+E30 for the three cases of blade health conditions using OBV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO10+EO20+EO30 33L
(5L+10C3)
30L
(7C3+1C9)
30L
(1C30)
0.02
0.04
0.06
0.08
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
0.08
30
210
60
240
90
270
120
300
150
330
180 0
0.02
0.04
0.06
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
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8.5.4 On-bearing data observations and discussion
Results observed from the polar plot presentation of data for short blades’ vibration
response during machine run-up in three cases of blade health conditions at
combinations of different EOs of (EO10+EO20), (EO10+EO40), (EO20+EO40) and
(EO10+EO20+EO30) are shown in Figures 8.16, 8.17, 8.18 and 8.19 respectively.
Results based on the combination EO10+EO20 show that in case (a) of healthy
conditions, the plot features a regular symmetrical distribution of 10 coupled leaves of 2
(C2); however in the faulty cases of one crack on one blade, case (b), and cracks on two
blades, case (c), the leaves are merged in one coupling of 20 leaves (C20) with different
shapes of leaves, but with the same total number of leaves (20) for the three cases, as
shown in Figure 8.16 and Table 8.14. Upon combination of (EO10+EO40), the number
of leaves changed from 35 in the healthy case (a) to 40 leaves for the cracked blade
cases (b) and (c), with different profile shapes and different numbers of coupled leaves
compared to case (a) of healthy conditions, as shown in Figure 8.17 and Table 8.15.
Likewise, for the combination of (EO20+EO40), the number of leaves changed from 35
in the healthy case (a) to 38 and 40 leaves in the case of one crack on one blade (b) and
the case of cracks on two blades (c), respectively, with different profile shapes and
different numbers of coupled leaves compared to case (a) of healthy blade conditions,
which featured leaves distributed regularly as couples of 2 (C2), as opposed to the plots
for the faulty blade cases that showed irregular distributions of large leaf couplings of
(C28) and (C34), as shown in Figure 8.18 and Table 8.16. Again, for the combination of
(EO10+EO20+EO30), the number of leaves changed from 33 for the healthy blade case
(a) to 30 leaves for the faulty blade cases (b) and (c), also with very different profile
shapes and numbers of coupled leaves compared to case (a) of healthy condition.
Leaves were distributed in regular couplings of 3 leaves (C3) in case (a), whereas for
faulty blade cases, leaves were distributed irregularly as one coupling of nine leaves
(C9) and 7 couples of 3 leaves (C3) in the case of one crack on one blade, and one
coupling of 30 leaves (C30) in the case of two cracked blades, as shown in Figure 8.19
and Table 8.17.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
175
Hence, from the results of the three cases of blade health conditions, it can be suggested
that the P3 method is able to detect blade conditions when used for analysis of OBV
data under different blade faults. Changes in the shape and number of individual leaves
and coupled leaves and also in the number of leaves in each coupling can give a good
induction of the blade health conditions and therefore can be a useful feature for BHM.
8.5.5 On-casing data analysis
The polar plot presentation (P3) method was used in this section to analyse shortblades’
vibration response obtained from the casing towards the rotating blades during machine
run-up by using an accelerometer in order to describe and compare blades’ dynamic
behaviour under three cases of blade health conditions; (a) healthy blades, (b) a crack on
one blade, and (c) cracks on two blades at different individual and combined EOs, as
shown in Figures 8.20-8.25.
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 8.20: Polar plot of acceleration (OCV) measured data of EO30 for cases: (a) healthy
blades, (b) crack on one blade and (c) cracks on two blades
Table 8-18: The numbers of single leaves and coupled leaves in the polar plot of EO30 for the
three cases of blade health conditions using OCV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO30 30L
(30L)
30L
(11L+1C19)
30L
(15L+1C15)
0.005
0.01
0.015
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
30
210
60
240
90
270
120
300
150
330
180 0
0.005
0.01
0.015
0.02
0.025
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 8.21: Polar plot of acceleration (OCV) measured data of EO10+EO30 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-19: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+E30 for the three cases of blade health conditions using OCV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO10+EO30 30L
(10L+10C2)
30L
(3C3+1C21)
30L
(6C3+1C12)
0.01
0.02
0.03
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
0.05
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
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Figure 8.22: Polar plot of acceleration (OCV) measured data of EO10+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-20: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+E40 for the three cases of blade health conditions using OCV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO10+EO40 21L
(1C21)
32L
(5C4+1C12)
40L
(3C4+1C28)
0.01
0.02
0.03
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
30
210
60
240
90
270
120
300
150
330
180 0
0.005
0.01
0.015
0.02
0.025
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
179
Figure 8.23: Polar plot of acceleration (OCV) measured data of EO20+EO40 for cases: (a)
healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-21: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+E40 for the three cases of blade health conditions using OCV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO20+EO40 20L
(20L)
37L
(7C2+1C23)
37L
(6C2+1C25)
0.01
0.02
0.03
0.04
30
210
60
240
90
270
120
300
150
330
180 0
0.005
0.01
0.015
0.02
30
210
60
240
90
270
120
300
150
330
180 0
0.005
0.01
0.015
0.02
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
180
Figure 8.24: Polar plot of acceleration (OCV) measured data of EO20+EO30+EO40 for cases:
(a) healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-22: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO20+EO30+E40 for the three cases of blade health conditions using OCV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO20+EO30+EO40 22L
(11C2)
34L
(6C3+4C4)
30L
(1C30)
0.01
0.02
0.03
0.04
0.05
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
181
Figure 8.25: Polar plot of acceleration (OCV) measured data of EO10+EO20+EO40 for cases:
(a) healthy blades, (b) crack on one blade and (c) cracks on two blades
Table 8-23: The numbers of single leaves and coupled leaves in the polar plot of combination
of EO10+EO20+E40 for the three cases of blade health conditions using OCV data
Case Healthy blades
(a)
Crack on one blade
(b)
Cracks on two blades
(c)
EO10+EO20+EO40 20L
(1C20)
32L
(2C2+4C3+4C4)
32L
(1C32)
0.02
0.04
0.06
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
0.04
30
210
60
240
90
270
120
300
150
330
180 0
0.01
0.02
0.03
30
210
60
240
90
270
120
300
150
330
180 0
(a)
(c) (b)
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
182
8.5.6 On-casing data observations and discussion
The results were obtained using a P3 method by analysingshortblades’vibration data
response during machine run-up, for the three different cases of blade health conditions
using individual and/or combinations of different EOs of (EO30), (EO10+EO30),
(EO10+EO40), (EO20+EO40), (EO20+EO30+EO40) and (EO10+EO20+EO40), as
shown in Figures 8.20, 8.21, 8.22, 8.23, 8.24 and 8.25, respectively. Results obtained at
engine order EO30 show that the number of leaves was the same for the three cases of
blade conditions: (a) healthy blades, (b) one crack on one blade, and (c) cracks on two
blades; however the shape of the leaves and the couplings between them were different
between the three cases. In the faulty blade cases, there were merged leaves in couplings
of 19 leaves (C19) and 15 leaves (C15) for cases (b) and (c), respectively, along with a
number of separate leaves. In the healthy blades case (a), the plot featured a regular set
of 30 single leaves without any couples, as shown in Figure 8.20 and Table 8.18. In the
combination EO10+EO30, all leaves in the healthy conditions case (a) were distributed
in coupled sets of 2 leaves (C2) in a regular distribution of a total of 10 couples spaced
by 10 further small single leaves. However, in the faulty cases featuring one crack on
one blade (case (b)) and cracks on two blades (case (c)), a number of leaves were
merged in coupled sets of 21 leaves (C21) and 12 leaves (C12) for cases (b) and (c)
respectively, with different shapes of leaves. The same total number of leaves for the
three cases was observed (30 leaves), as shown in Figure 8.21 and Table 8.19. In the
combination of (EO10+EO40), the number of leaves changed from 21 leaves for the
healthy case (a) to 32 leaves for the cracked blade case (b) and 40 leaves for the cracked
blade case (c), with different shapes and numbers of coupled leaves compared to case
(a) of healthy conditions, as shown in Figure 8.22 and Table 8.20. Likewise, in the
combination of (EO20+EO40), the number of leaves changed from 20 leaves for the
healthy case (a) to 37 leaves for the two faulty blade cases (b) and (c), with different and
irregular profile shapes and different numbers of coupled leaves compared to case (a) of
healthy conditions, which featured individual leaves distributed in a very regular
arrangement, as shown in Figure 8.23 and Table 8.21. Finally, in the two remaining
combinations of (EO20+EO30+EO40) and (EO10+EO20+EO40), the number of leaves
also changed from 22 and 20 leaves respectively in the healthy blade case (a), to 30 or
more leaves for the faulty blade cases (b) and (c) in the two combinations, showing a
CHAPTER 8 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
183
regular shape of leaves in the healthy case (a) and different irregular profile shapes of
leaves and different numbers of coupled leaves for the faulty blade cases (b) and (c), as
shown in Figures 8.24 and 8.25 and Tables 8.22 and 8.23 respectively for the two
combinations.
Hence, from the results of three cases of blade health conditions, it can be suggested
that the P3 method is able to detect blade conditions when used to analyse on-casing
vibration data under different blade fault conditions, based on changes in the shape and
number of individual and coupled leaves and the number of leaves in each coupling,
which provides a good indication of the blade health conditions and therefore, this tool
can be useful as a feature of BHM.
8.6 Summary
In this chapter, a novel signal processing application based on a P3 method has been
described and applied to the analysis of measured vibration data for long and short
blades using three different measurements: shaft torsional vibration based on IAS
signals, OBV and OCV during machine run-up operation. This method was applied in
order to diagnose rotating blades’ health conditions with and without faults. The
experiments were conducted for three different blade health conditions: healthy blades
with mistuned effects, blade root looseness and cracks on blades. The data were
analysed using the novel signal processing technique P3 at different engine orders
relatedtotheblades’resonancefrequencyandalsoatdifferentcombinationsofengine
orders in order to describe and detect blade health conditions under different blade
faults. From the results, it was observed that the P3 is useful for the diagnosis of blade
condition. The differences in the shape and number of separate and coupled leaves and
the differences in the number of leaves in each coupling between healthy and faulty
blade cases are features that provide a good indication for detecting blade faults. This
was observed based on all the data from the three measurements mentioned above.
Therefore, it is suggested that this novel application can be useful as a means of
diagnosis in the process of BHM.
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
184
CHAPTER 9
MATHEMATICAL MODEL
9.1 Introduction
A simple mathematical model was created for a shaft-disc-blades system in order to
derive the equations of motion for the system for a single degree of freedom (SDOF)
representing the 8 blades rotor disc. A rotor of 8 blades was created to understand the
dynamic behaviour of healthy and cracked blades in transient motion using a simplified
method. This chapter provides further understanding of blades dynamics observed in the
experiments presented in Chapters 4, 5, 6 and 8.
9.2 Simple mathematical model
The model of the 8-blades rotor is shown in Figure 9.1. It represents the parameters
including the mass of the rotor, mr, supported by the shaft that has stiffness, kr, and the
damping, c, is considered with 8 blades to understand the experimental behaviour of the
system. The rotor is initially modelled as a SDOF system connected with 8 SDOF
systems representing the 8 blades of masses (mb1, mb2, …..mb8), stiffness values (kb1,
kb2,….kb8) and damping values (cb1, cb2,….cb8). The values of the rotor parameters and
the blade masses [Mb], corresponding stiffness [Kb] and damping [Cb] are chosen such
that the model gives natural frequencies of long blades close to the experimental values.
Table 9.1 provides the values of the parameters used in the model. The natural
frequencies of the rotor are computed to be 97.22 Hz and 118.15 Hz to 131.66 Hz for
the blades with mistuned effects, which are very close to the experimental values. The
damping in the mathematical model is assumed to be 0.1% and 0.5% for the rotor and
blades, respectively. The rotor is rotating with angular velocity (ω). The unbalanced
responses for the rotor are then calculated during the run-up speed from 600 rpm (10Hz)
to 1800 rpm (30Hz) for 30 seconds for the following two cases:
1- Healthy but mistuned blade conditions in frequency band of 118.15 Hz to131.66 Hz.
2- 10% crack on Blade 2 on the bladed disc.
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
185
Table 9-1: Rotor/ blade parameters used in the mathematical model
Rotor
Rotor
Mass (mr) Stiffness (Kr) Damping ratio (Cr)
50.00 2.1544 x107 1.0000 x10
-3
Blades
Blade no. Blade mass, (mb) Blade Stiffness (kb)
1 1.00 6.2147 x105
2 1.00 6.5630 x105
3 1.00 5.4632 x105
4 1.00 6.3019 x105
5 1.00 6.1559 x105
6 1.00 5.7188 x105
7 1.00 5.9537 x105
8 1.00 6.1623 x105
Figure 9.1: Simplified rotor model with 8 blades
9.3 Crack simulation
The crack on Blade 2 is simulated a breathing of the 10% of blade width, while the
response is estimated by assuming the change in Blade 2 stiffness when the
displacement in the blade is positive (open crack) using the equation:
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
186
kkk 2b …………………………(9.1)
and when the displacement in the blade is zero (normal position) or the displacement in
the blade is negative (closed crack), the following equation is used:
kk 2b …………………………(9.2)
where k is the initial value of blade stiffness and the change in the stiffness, k10.0k
for 10% crack on Blade 2.
9.4 Equation of motion
ByapplyingNewton’s second lawofmotion (m .a=F), theequationsof translation
motion for the system are obtained from the summation of forces using the free force
diagram as illustrated in Figure 9.2, which can easily determine the summation of forces
for the system (m = mass, a = acceleration and F = Force).
9.4.1 Shaft forces components
The bounce of forces for the shaft is determined from the free body diagram as shown in
Figure 9.2, and is given by:
mr xr = ∑ Fi8i=1 …………………….(9.3)
mr xr = ∑ [kbi(xbi − xr) + cbi(xbi − xr)]8i=1 −
kr
2 xr −
kr
2 xr − c xr ……….(9.4)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
187
Figure 9.2: Free body diagram for rotor forces
The parameters above are defined as:
mb1 - mb8: mass of blades 1-8
kb1 - kb8: stiffness of blades 1-8
cb1 - cb8: damping of blades 1-8
xb1 - xb8: acceleration of blades 1-8
xb1 -xb8: velocity of blades 1-8
xb1 -xb8: displacement of blades 1-8
mr: rotor mass
kr: rotor stiffness
cr: rotor damping
xr : rotor acceleration
xr : rotor velocity
xr : rotor displacement
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
188
9.4.2 Blades forces components
The equations of motion of the rotor with 8 blades are determined from the free body
diagram as shown in Figure 9.2 and are given by force equations:
mb xbi = ∑ Fi8𝑖=1 …………………….(9.5)
where, (i=1,2,……….,8)
9.4.3 The unbalancing disc force components
The unbalancing forces in rotating machines are the main factors causing vibration. The
rotor response due to mass imbalance excitation and the unbalanced forces equations
can be written as follows, as shown in Figure 9.3.
𝐹 = 𝑚 r 𝜔2 …………………….(9.6)
𝐹𝑢𝑛𝑏 = mu r𝜔2 ∗ sin(𝜔t + 30) …………………….(9.7)
mr xr + 𝐹𝑢𝑛𝑏 = mu r𝜔2 ∗ sin(𝜔t + 30) …………………….(9.8)
In this model, r = 0.02 m and unbalanced mass (mu =0.01 kg) are chosen.
where:
mr : rotor mass (kg)
mu : unbalancing mass (kg)
r : radius (m)
𝜔 : angular velocity (RPS)
Funb : unbalanced force (N)
andthesystem’snaturalfrequencyinrpscanbedeterminedusingtheequation:
𝜔 = √𝐾
𝑚 …………………….(9.9)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
189
Figure 9.3: Free body diagram of unbalanced mass disc forces in x-y plane
9.5 Dynamic equations of the mathematical model
The dynamic equations of the model including unbalanced disc forces can be solved
using matrices. The number of equations of transient motion for the system is 9,
including rotor motion in Equation 9.4, blades motion in Equation 9.5 and unbalancing
force in Equation 9.7.
The general equation of motion for the system using the matrix system can be derived
by:
[𝑀 ]. {��} + [𝐶 ]. {��} + [𝐾 ]. {𝑋} = {𝐹𝑢𝑛𝑏} …………………….(9.10)
Where:
{��} : The acceleration vector
{��} : The velocity vector
{𝑋} : The displacement vector
{𝐹𝑢𝑛𝑏} : The unbalanced force vector
[M] : The mass matrix
[C] : The damping matrix
[K] : The stiffness matrix
Funb= mu r 2
Rotating disc
mu
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
190
The modal analysis of the rotor with 8-bladed disc are calculated using MATLAB
software by solving the equation of modal analysis using the matrix equation 9.11 to
identified the mode shapes and natural frequencies.
[[𝐾] − 𝜔𝑛2 [𝑀] ]. {𝜃} = 0 …………………….(9.11)
where:
[M]: mass matrix
[K]: Stiffness matrix
{𝜃}: is the mode shapes matrix, 𝜔𝑛 is the nth angular natural frequency, 2𝜋𝑓𝑛, where 𝑓𝑛
is the nth natural frequency
The natural frequencies of blades and shaft are obtained using the mathematical model
analysis and are listed in Table 9.2.
Table 9-2: First mode shape frequencies for blades and shaft obtained using the mathematical
model
Blade No. Natural frequency (Hz) Blade No. Natural frequency (Hz)
1 118.15 5 125.88
2 119.83 6 128.05
3 122.33 7 130.29
4 124.17 8 131.66
Shaft Natural Frequency (Hz) 97.25
The modal analysis results were observed to be close to the experimental results
obtained using frequency response function (FRF), experimental test as listed in Table
3.8.
9.5.1 Derivation of the matrices system
The equations of motion of the rotor with eight blades model of SDOF with 9 equations
of motion can be established when the contribution from rotation degrees of freedom is
included in the mass, stiffness and damping matrices using Equations 9.4, 9.5 and 9.7,
which can be used to derive the matrices system of the model and then solved by
MATLAB.
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
191
The mass matrix for the system is derived as:
[𝑀]=
[ 𝑚𝑏1
00000000
0𝑚𝑏2
0000000
00𝑚𝑏3
000000
000𝑚𝑏4
00000
0000𝑚𝑏5
0000
00000𝑚𝑏6
000
000000𝑚𝑏7
00
0000000𝑚𝑏8
0
00000000𝑚𝑟]
…………………….(9.12)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
192
And the stiffness matrix is derived as:
[𝐾] =
[ 𝑘𝑏10000000
−𝑘𝑏1
0𝑘𝑏2000000
−𝑘𝑏2
00𝑘𝑏300000
−𝑘𝑏3
000𝑘𝑏40000
−𝑘𝑏4
0000𝑘𝑏5000
−𝑘𝑏5
00000𝑘𝑏600
−𝑘𝑏6
000000𝑘𝑏70
−𝑘𝑏7
0000000𝑘𝑏8 −𝑘𝑏8
−𝑘𝑏1−𝑘𝑏2−𝑘𝑏3−𝑘𝑏4−𝑘𝑏5−𝑘𝑏6−𝑘𝑏7−𝑘𝑏8
(𝑘𝑏1 + 𝑘𝑏2 + 𝑘𝑏3 + 𝑘𝑏4 + 𝑘𝑏5 + 𝑘𝑏6 + 𝑘𝑏7 + 𝑘𝑏8 − 𝑘𝑟)]
…………….....….(9.13)
The derivation of damping matrix is as follows:
[𝐶] =
[ 𝑐𝑏10000000
−𝑐𝑏1
0𝑐𝑏2000000
−𝑐𝑏2
00𝑐𝑏300000
−𝑐𝑏3
000𝑐𝑏40000
−𝑐𝑏4
0000𝑐𝑏5000
−𝑐𝑏5
00000𝑐𝑏600
−𝑐𝑏6
000000𝑐𝑏70
−𝑐𝑏7
0000000𝑐𝑏8 −𝑐𝑏8
−𝑐𝑏1−𝑐𝑏2−𝑐𝑏3−𝑐𝑏4−𝑐𝑏5−𝑐𝑏6−𝑐𝑏7−𝑐𝑏8
(𝑐𝑏1 + 𝑐𝑏2 + 𝑐𝑏3 + 𝑐𝑏4 + 𝑐𝑏5 + 𝑐𝑏6 + 𝑐𝑏7 + 𝑐𝑏8 − 𝑐𝑟)]
…………………...……. (9.14)
The vectors of acceleration, velocity, displacement and force are derived as:
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
193
Acceleration Vector {��} =
{
��𝑏1��𝑏2��𝑏3��𝑏4��𝑏5��𝑏6��𝑏7��𝑏8��𝑟 }
…………………….(9.15)
Velocity Vector {��} =
{
��𝑏1��𝑏2��𝑏3��𝑏4��𝑏5��𝑏6��𝑏7��𝑏8��𝑟 }
…………………….(9.16)
Displacement Vector {𝑋} =
{
𝑥𝑏1𝑥𝑏2𝑥𝑏3𝑥𝑏4𝑥𝑏5𝑥𝑏6𝑥𝑏7𝑥𝑏8𝑥𝑟 }
…………………….(9.17)
And the unbalanced disc force vector is
{𝐹𝑢𝑛𝑏} =
{
00000000
mu 𝜔2 r ∗ sin(𝜔t + 30)}
………….(9.18)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
194
The equation of motion for the unbalancing disc of transient motion of the rotor can be
derived using Equation 9.10; by substituting Equations 9.4, 9.5 and 9.7 into Equation
9.10, the general equation of motion for the model can be obtained as shown in
Equation 9.19
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
195
[ 𝑚𝑏1
00000000
0𝑚𝑏2
0000000
00𝑚𝑏3
000000
000𝑚𝑏4
00000
0000𝑚𝑏5
0000
00000𝑚𝑏6
000
000000𝑚𝑏7
00
0000000𝑚𝑏8
0
00000000𝑚𝑟]
.
{
��𝑏1��𝑏2��𝑏3��𝑏4��𝑏5��𝑏6��𝑏7��𝑏8��𝑟 }
+
[ 𝑐𝑏10000000
−𝑐𝑏1
0𝑐𝑏2000000
−𝑐𝑏2
00𝑐𝑏300000
−𝑐𝑏3
000𝑐𝑏40000
−𝑐𝑏4
0000𝑐𝑏5000
−𝑐𝑏5
00000𝑐𝑏600
−𝑐𝑏6
000000𝑐𝑏70
−𝑐𝑏7
0000000𝑐𝑏8 −𝑐𝑏8
−𝑐𝑏1−𝑐𝑏2−𝑐𝑏3−𝑐𝑏4−𝑐𝑏5−𝑐𝑏6−𝑐𝑏7−𝑐𝑏8
(𝑐𝑏1 + 𝑐𝑏2 + 𝑐𝑏3 + 𝑐𝑏4 + 𝑐𝑏5 + 𝑐𝑏6 + 𝑐𝑏7 + 𝑐𝑏8 − 𝑐𝑟)]
.
{
��𝑏1��𝑏2��𝑏3��𝑏4��𝑏5��𝑏6��𝑏7��𝑏8��𝑟 }
+
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
196
[ 𝑘𝑏10000000
−𝑘𝑏1
0𝑘𝑏2000000
−𝑘𝑏2
00𝑘𝑏300000
−𝑘𝑏3
000𝑘𝑏40000
−𝑘𝑏4
0000𝑘𝑏5000
−𝑘𝑏5
00000𝑘𝑏600
−𝑘𝑏6
000000𝑘𝑏70
−𝑘𝑏7
0000000𝑘𝑏8 −𝑘𝑏8
−𝑘𝑏1−𝑘𝑏2−𝑘𝑏3−𝑘𝑏4−𝑘𝑏5−𝑘𝑏6−𝑘𝑏7−𝑘𝑏8
(𝑘𝑏1 + 𝑘𝑏2 + 𝑘𝑏3 + 𝑘𝑏4 + 𝑘𝑏5 + 𝑘𝑏6 + 𝑘𝑏7 + 𝑘𝑏8 − 𝑘𝑟)]
.
{
𝑥𝑏1𝑥𝑏2𝑥𝑏3𝑥𝑏4𝑥𝑏5𝑥𝑏6𝑥𝑏7𝑥𝑏8𝑥𝑟 }
=
{
00000000
mu 𝜔2 r ∗ sin(𝜔t + 30)}
…………………….(9.19)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
197
9.6 Results and observations
The run-up acceleration responses of the long blades using MATLAB software have
been calculated using Equation 9.19. The typical acceleration signal of healthy and
cracked blade responses is shown in Figure 9.4 (a) and (b). A contour plot is then
generated in order to discover blade resonance (BR) in the frequency regions for both
cases of healthy and cracked blade. The results that showed a big significant difference
in BR regions between the cases of healthy blades compared to cracked blades for both
blade resonances of 1xBR and 2xBR, as shown in Figures 9.5 and 9.6 respectively.
Responses were then order tracked at EO5, which is used to obtain the vibration
response of the rotor, as shown in Figure 9.7 (a) and (b). The results showed the
presence of a peak around 97 Hz in Figure 9.7, indicating the critical speed of the rotor.
It is also evident from Figure 9.7 (a) that the healthy blade case gives a banded peak in
the BR region; however, two distinct peaks can be seen for the cracked blade case, as
shown in Figure 9.7 (b). Hence, this simple analysis of the mathematical model supports
and verifies the experimental observations related to blade damage detection. Therefore,
the presence of distinct multiple peaks in the BR region for cracked blades instead of
one distinct peak for healthy blades can be considered a useful feature to identify blade
fault (s).
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
198
Figure 9.4: Typical acceleration run-up responses signal, (a) Healthy blades, (b) Crack on
Blade
0 5 10 15 20 25 30-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Time (sec)
Accele
ra
tio
n (
m/s
2)
0 5 10 15 20 25 30-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
Time (sec)
Am
pli
tud
e, A
ccele
ra
tio
n (
m/s
2)
(a)
(b)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
199
Figure 9.5: Typical contour plot of BR frequency (1xBR), (a) Healthy blades, (b) Crack on
Blade
Frequency (Hz)
Tim
e (
sec)
100 120 140 160 180
2
4
6
8
10
12
14
16
18
10
20
30
40
50
60
70
80
Frequency (Hz)
Tim
e (
sec)
100 120 140 160 180
2
4
6
8
10
12
14
16
18
10
20
30
40
50
60
70
80
(b)
(a)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
200
Figure 9.6: Typical contour plot of BR frequency (2xBR), (a) Healthy blades, (b) Crack on
Blade
Frequency (Hz)
Tim
e (s
ec)
200 220 240 260 280
2
4
6
8
10
12
14
16
18
5
10
15
20
25
30
35
40
Frequency (Hz)
Tim
e (
sec)
200 220 240 260 280
2
4
6
8
10
12
14
16
18
5
10
15
20
25
30
35
40(b)
(a)
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
201
Figure 9.7: Typical estimated acceleration responses at EO5, (a) Healthy blades, (b) Crack on
Blade 2
9.7 Summary
A simple mathematical model of a rotor with 8-blade configuration is presented in this
chapter. The equations of transientmotion of the rotorwere derived usingNewton’s
CHAPTER 9 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
202
second law of motion for the rotor and blades, including the unbalanced force of the
rotating disc. The modal analysis for the rotor was computed, and the natural
frequencies for the 8 blades and shaft identified. Nine equations of motion of SDOF
were solved using MATLAB, whereby the dynamic acceleration responses of the model
for healthy and cracked blades were extracted. The results of simple analysis of the
mathematical model verified and supported the experimental observations.
CHAPTER 10 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
203
CHAPTER 10
CONCLUSIONS AND FUTURE WORK
10.1 Overview
Rotating blades are considered to be one of the most common cause of failures in
rotating machinery. The blade failure modes normally occur as a result of cracking, high
cycle fatigue (HCF), blade rubbing, blade root looseness, and degradation from erosion
and corrosion. Early fault detection is therefore necessary to reduce blade related
failures and hence there is a need for a reliable, indirect and simple measurement
method to be used for blade health monitoring (BHM). There are some measurements
that have recently been used for BHM, such as blade tip timing (BTT) and measurement
of strain on the blades during machine operation. However, both BTT and strain
measurement methods are intrusive and expensive. Consequently, there is an imperative
need for a reliable and simple but robust method to meet the requirements of BHM.
For these reasons, this study attempted to demonstrate that vibration measurements such
as shaft torsional vibrations based on instantaneous angular speed (IAS), on-bearing
vibrations (OBV) and on-casing vibrations (OCV) could detect changes in blade(s)
dynamic behaviour due to blade(s) fault conditions and could be useful for BHM. In this
study, extensive experiments were performed on a small test rig having a rotor with a
single stage bladed disc holding 8-blades during machine run-up, in addition to a
mathematical model which was also developed in order to describe blade(s) behaviour.
The results show that the mathematical model supports the experimental work in
detecting and diagnosing blade health conditions and determining blade faults due to
mistuned effects, blade root looseness and blade cracks. All measurement methods can
be considered useful for BHM. Engine order tracking (EOT) method was shown to be
useful for blade fault detection during the machine run-up. In addition, a polar plot
presentation related to blade order angle and amplitude of vibration signal showed the
changes in vibration response of blade(s) behaviour, and was a practically useful
technique to detect blade faults.
CHAPTER 10 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
204
10.2 Review of project objectives, achievements and contributions
The main achievements and of this research work and its contributions to knowledge are
described and presented below with matched objectives of this project study as
presented in Section 1.3.
Objective 1
To design and manufacture a test rig suitable for simulating rotating machine blades and
detecting blade faults due to vibration problems arising from mistuned blades, blade
root looseness and blade cracks using vibration condition monitoring.
Achievement and contribution 1
The required small test rig was designed and manufactured with dimensions in
accordance with a finite element model (FEM) analysis results. The rig was designed to
simulate rotating machine blades in steam or gas turbines in order to be used for detect
blade(s) faults of mistuned blades, blade root looseness and cracks in blade(s). The rig
was designed to allow measurements and to be equipped with a speed controller for
operation at different speeds. Furthermore, a simulation of blade faults, such as blade
looseness and blade cracks, was done for different experiments.
Objective 2
To apply the following measurements during machine running-up and/or running-down
operation in order to examine blade vibration fault detection:
- Torsion vibration on shaft using incremental shaft encoder.
- Measurements of on bearing pedestal vibrations using accelerometers.
- Measurements of on casing vibrations using accelerometers.
Achievement and contribution 2
The three measurements of the shaft torsional vibration based on IAS, OBV and OCV
were applied on the test rig in order to discover blade(s) dynamic behaviour during
machine run-up operation to be used for BHM.
CHAPTER 10 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
205
Objective 3
To investigate blade vibration behaviour with these faults: blade mistuning effects,
blade root looseness and cracked blades, using the three measurements stated in
Objective 2.
Achievement and contribution 3
(1) The results obtained from OBV signals provided a good indication for long
bladesincasesofbladerootloosenessandcrackedblade(s),andalsoprovided
somegoodindicationforshortbladesonlyinthecaseofmorethanonecracked
bladein thebladeddisc. This was apparent in the presence of distinct multiple
peaks in the blade resonance (BR) region in the case of damaged blades instead
of a single peak in the BR region in the case of healthy blade(s).
(2) TheresultsobtainedfromOCVsignals forshortbladesshowedno changes in
the BR region in the case of cracked blades compared with blades in healthy
conditions. The results showed only one distinct peak of BR for all cases,
healthy and faulty.
(3) The results of IAS signals provided a good indication to detect blade health
conditionsforbothlongandshortblades.Thiswasobservedasasinglebanded
peakintheBRregioninthecaseofhealthybladescomparedtomorepeaksin
theBRregioninthecasesofbladedamageconditions.
Objective 4
To compare and analyse the results obtained using the proposed three measurements
and to investigate which measurement is most useful to detect and diagnose blade
health conditions.
Achievement and contribution 4
(1) OCVcouldnotbefoundtobeagoodmeasurement forBHMfortherigbutit
maybeusefulforrotatingmachinesinplants.ThiswasbecausetheOCVgave
greatly amplified BR for the laboratory rig and the casing in the rig was not
designedproperly.
(2) OBV provided a good indication to detect long blades’ faults. However, the
resultsforshortbladesonlyprovidedsomegoodindicationforfaultdetectionin
CHAPTER 10 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
206
thecaseswhenmorethanonebladewascrackedinthebladeddisc.Hence,the
presence of distinct multiple peaks in the BR region can be considered as a
featureforBHMinthesecasesbasedonthismeasurement.
(3) IASprovidedagoodindicationtodetectbladehealthconditionsforbothshort
and long blades.Hence, the presence of the distinctmultiple peaks in theBR
regioncanbeconsideredasahighlyusefulfeaturetobeusedforBHM.
Objective 5
To select the best measurement for monitoring purposes with a unique signal processing
method to identify blade mistuning effects, blade root looseness and cracked blades.
Achievement and contribution 5
From thecomparativestudydescribed inChapter7, the resultsobserved indicate that
the shaft IAS signal seems to providemuch better indication for BHM compared to
OBV and OCV measurements. In addition, a novel and unique signal processing
methodtorepresentsthesummationoftimesynchronizingaveraging(TSA)datarelated
todifferentengineordersinthepolarplotswasusedfordiagnosis,basedonvibration
signalsobtainedfromOBV,OCVandIAStodetermineblade(s)healthconditionsdue
toblademistuningeffects,bladeroot loosenessandcrackedblade(s).Thenovelpolar
plotmethodprovidedagoodindicationofpotentialtobeutilisedasatoolforBHM.
Objective 6
To execute a theoretical simulation analysis to compare the experimental observations
with simulations in order to understand blade(s) vibration dynamic behaviour.
Achievement and contribution 6
A simple mathematical model of 8 blades single stage configuration was developed.
The acceleration responses were calculated for the transient motion of run-up for cases
of healthy and cracked blade(s), and then short time Fourier transform (STFT) contour
plots and order tracking were carried out on the acceleration response to obtain the
vibration response of the rotor. The results of analysis of the mathematical model
verified and supported the experimental observations and the model can be considered a
useful feature to identify blade fault(s).
CHAPTER 10 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
207
10.3 Overall conclusion
The shaft torsional vibration based on IAS signals provided the highest quality
indicationoffaultdetectioncomparedtoOBVandOCVmeasurementsforthepurpose
of diagnosing blade health conditions in the cases of: healthy blades with mistuned
effect, blade root looseness and cracked blade(s). The results were based on the
observationofthepresenceofdistinctmultiplepeaksintheBRregionforfaultyblades
comparedtoasinglebandedpeakinthecasesofhealthyblades,usingtheanalysisof
EOTmethod.Thedistinct feature for the faultyblades is alsoclearlyobserved in the
proposednovelpolarplotsusingTSAdataofthebladeresonancerelatedEOs.Hence,
IASsignalsarehighlyrecommendedasameasurementtobeusedasafeatureofBHM
basedoncurrentresearchstudy.
10.4 Novel features
The research study presented in this thesis incorporated a number of important aspects
that are novel and have not been previously implemented by other researchers or
practitioners. These aspects of novelty are summarised below.
Novelty 1
The author believes that the way in which IAS vibration signals were used for blade
fault diagnosis and examined using experimental tests with further signal processing
analysis techniques supported by a simplified mathematical model to successfully
predict faults is novel.
Novelty 2
The author believes that the polar plot presentation method, presented in Chapter 8,
which was based on a 2D polar coordinates system and was used for the detection and
diagnosis of blade health conditions due to different blade faults, is entirely novel. In
this approach, a polar plot of 360° angular coordinates related to theblades’orderangle
andtheblades’vibrationsignalamplitudeisused.Theradiusofthesystemcorresponds
to the vibration signal amplitude and the 360° angular coordinates are used to represent
the 360° angular duration of the shaft’s full rotation. The approach of polar presentation
is used for the 8-blade system, which means that the angle between every two blades is
45°. This presentation method is unique and no work has been carried out previously
CHAPTER 10 Ahmed. Gubran 2015, PhD Thesis, The University of Manchester, UK
208
using a polar plot presentation as a tool for the detection and diagnosis of blade health
conditions for the purpose of BHM.
10.5 Future work
There is a number of possible future research study that can enhance the confidence in
the proposed detection and diagnosis of faults and enhance BHM. These are listed
below.
1- To investigate the use of multi-stages of bladed discs with and without blade
faults in different stages in order to diagnose and detect blade faults at different
stages.
2- To investigate the use of wireless measurement systems on the bladed disc itself
to determine the ability of the method to diagnose blade health conditions due to
blade(s) faults.
3- To investigate twisted blades like real turbine blades with and without faults in
order to diagnose and detect blade health condition with accurate diagnosis of
blade faults.
4- To examine the vibration measurements which were used in this study for
investigating blade faults in rotating machines in plant.
5- To investigate the applicability of shaft torsional vibration measurement to
locate cracked or faulty blade(s) in a bladed disc whilst the machine is in
operation.
6- To conduct further experiments using on-casing measurement to investigate the
applicability of this method for BHM. This is easy for instrumentation and
measurement.
APPENDICES
209
APPENDICES
Appendix A
Combined figures of on-bearing vibration data analysis results for blades health
condition cases:
(a) Long blades
Figure A 1: Combined curves of bearing measured acceleration data for long blades for engine
order EO5 spectra: Case (1) healthy blades, (2-ii) blade 5 root looseness, (3-ii) cracks on
blades 2 & 4
110 115 120 125 130 1350
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
-4
Frequency (Hz)
EO
5, A
ccele
ra
tio
n (
m/s
2)
Case 1
Case 2 (ii)
Case 3 (ii)
APPENDICES
210
Figure A 2: Combined curves of bearing measured acceleration data for long blades for engine
order EO10 spectra: Case (1) healthy blades, (2-ii) blade 5 root looseness, (3-ii) cracks on
blades 2 & 4
220 230 240 250 260 2700
0.5
1
1.5
2
2.5
3x 10
-4
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
Case 1
Case 2 (ii)
Case 3 (ii)
APPENDICES
211
(b) Short blades
Figure A 3: Combined curves of bearing measured acceleration data for short blades for engine
order EO10 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4
220 225 230 235 240 245 250 255 2600
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (
m/s
2)
Case 1
Case 3 (i)
Case 3 (ii)
APPENDICES
212
Figure A 4: Combined curves of bearing measured acceleration data of short blades for engine
order EO20 spectra for: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4
440 450 460 470 480 490 500 510 5200
0.005
0.01
0.015
0.02
0.025
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
Case 1
Case 3 (i)
Case 3 (ii)
APPENDICES
213
Appendix B
Combined figures of on-casing vibration data analysis results for blades health
conditions cases for short blades:
Figure B 1: Combined curves of casing measured acceleration data for short blades for engine
order EO10 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4
220 225 230 235 240 245 250 255 2600
0.005
0.01
0.015
0.02
0.025
0.03
Frequency (Hz)
EO
10
, A
ccele
ra
tio
n (m
/s2)
Case 1
Case 3 (i)
Case 3 (ii)
APPENDICES
214
Figure B 2: Combined curves of casing measured acceleration data for short blades for engine
order EO20 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on
blades 2 & 4
440 450 460 470 480 490 500 510 520 530 5400
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
Frequency (Hz)
EO
20
, A
ccele
ra
tio
n (
m/s
2)
Case 1
Case 3 (i)
Case 3 (ii)
APPENDICES
215
Appendix C
Combined figures of shaft torsional vibrations based on instantaneous angular speed
signal data analysis results for blades health conditions cases:
(a) Long blades
Figure C 1: Combined curves of measured encoder (IAS) data for engine order EO5 spectra:
Case (1) healthy blades, Case (2-i) blade 1 root looseness, Case (2-ii) blade 5 root looseness, (3-
i) crack on blade 4, (3-ii) cracks on blades 2 & 4, (3-iii) crack on blade 2, (3-iv) crack on blade 4
after 100 minute of running
115 120 125 130 1350
0.05
0.1
0.15
0.2
0.25
0.3
Frequency (Hz)
EO
5, S
peed
(R
PS
)
Case 1
Case 2 (i)
Case 2 (ii)
Case 3 (i)
Case 3 (ii)
Case 3 (iii)
Case 3 (iv)
APPENDICES
216
Figure C 2: Combined curves of measured encoder (IAS) data for engine order EO10 spectra:
Case (1) healthy blades, Case (2-i) blade 1 root looseness, Case (2-ii) blade 5 root looseness, (3-
i) crack on blade 4, (3-ii) cracks on blades 2 & 4, (3-iii) crack on blade 2, (3-iv) crack on blade 4
after 100 minute of running
230 235 240 245 250 255 260 265 2700
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency (Hz)
EO
10
, S
peed
(R
PS
)
Case 1
Case 2 (i)
Case 2 (ii)
Case 3 (i)
Case 3 (ii)
Case 3 (iii)
Case 3 (iv)
APPENDICES
217
Figure C 3: Combined curves of measured encoder (IAS) data for engine order EO15 spectra:
Case (1) healthy blades, Case (2-i) blade 1 root looseness, Case (2-ii) blade 5 root looseness, (3-
i) crack on blade 4, (3-ii) cracks on blades 2 & 4, (3-iii) crack on blade 2, (3-iv) crack on blade 4
after 100 minute of running
350 360 370 380 390 4000
0.05
0.1
0.15
0.2
0.25
0.3
Frequency (Hz)
EO
15
, S
peed
(R
PS
)
Case 1
Case 2 (i)
Case 2 (ii)
Case 3 (i)
Case 3 (ii)
Case 3 (iii)
Case 3 (iv)
APPENDICES
218
(b) Short blades
Figure C 4: Combined curves of measured encoder (IAS) data of short blades for engine order
EO10 spectra: Case (1) healthy blades, (3-i) crack on blade 4, (3-ii) cracks on blades 2 & 4
220 225 230 235 240 245 250 255 2600
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Frequency (Hz)
EO
10
, S
peed
(R
PS
)
Case 1
Case 3 (i)
Case 3 (ii)
REFERENCES
219
REFERENCES
1. Thompson, S. Tribology Solutions: Section 1- Computational Systems Incorporated (CSI).
2000: Knoxville, Tennessee, USA.
2. Oceanfront Engineering, Nuclear Turbine rotor 03. 2010; Available from:
http://www.oceanfrontengineering.com/images/NuclearPower/resources/images/large/Nucle
ar%20Turbine%20Rotors%2003.jpg [Accessed on: 01/02/2015].
3. High pressure turbine blade of Rolls-Royce turbofan. 2008; Available from:
http://farm4.staticflickr.com/3038/2839417104_762cdc2e8a_z.jpg [Accessed on:
01/02/2015].
4. Lacey, S., Vibration monitoring of the internal centreless grinding process Part 2:
experimental results. Proceedings of the Institution of Mechanical Engineers, Part B:
Journal of Engineering Manufacture, 1990. 204(2): p. 129-142.
5. Carter, T.J., Common failures in gas turbine blades. Engineering Failure Analysis, 2005.
12(2): p. 237-247.
6. Meher-Homji, C., Blading vibration and failures in gas turbines-Part A: blading dynamics
and the operating environment. ASME Paper, 1995(95-GT): p. 418.
7. BK Industrial Group Pty Ltd., Gas Turbine. 2012; Available from:
http://www.bkig.com.au/wp-content/uploads/2012/11/Gas-Turbine-300x296.jpg [Accessed
on: 01/02/2015].
8. Poursaeidi, E., et al., Investigation of choking and combustion products’ swirling frequency
effects on gas turbine compressor blade fractures. Journal of Fluids Engineering, 2013.
135(6): p. 061203.
9. Power Profit, Pitting and corrosion in steam turbine blades are accelerated by cycling
operation. n.d.; Available from:
http://powerprofit.fortnightly.com/sites/default/files/styles/article_embed_image/public/130
905-Steam-turbine-pitting-from-corrosion-fatigue_0.jpg?itok=0lJdcnSY [Accessed on:
01/02/2015].
10. Plant Services, An example of solid particle erosion in steam turbine. 2011; Available from:
http://www.plantservices.com/assets/Media/1109/maintenance-slows-degradation-of-
rotating-equipment2.jpg [Accessed on: 01/02/2015].
11. Lambda Technologies Group, Corrosion pitting in steam turbine blades. 2012; Available
from: http://www.lambdatechs.com/pitting.html [Accessed on: 01/02/2015].
12. Steam & Power, LP turbine blade tip-thinning. 2008; Available from:
http://www.steamforum.com/picture.asp?p=/pictures/Turbine%20Blading1.JPG [Accessed
on: 01/02/2015]
13. Energodiagnostika, The example of the blades and shroud strip failure due to deficit of
thermal compensation. n.d.; Available from:
REFERENCES
220
http://www.energodiagnostika.com/Content/Images/Pages/app_mmm_shround_2_en.jpg
[Accessed on: 01/02/2015].
14. High pressure turbine blade of Rolls-Royce turbofan. 2008; Available from:
http://farm4.staticflickr.com/3038/2839417104_762cdc2e8a_z.jpg [Accessed on:
01/02/2015]
15. Harris, C.M., A.G. Piersol, and T.L. Paez, Harris’ shock and vibration handbook. Vol. 5.
2002: McGraw-Hill, New York.
16. Rao, B.K.N., Handbook of condition monitoring. 1996: Elsevier Advanced Technology,
Oxford.
17. Edwards, S., A. Lees, and M. Friswell, Fault diagnosis of rotating machinery. Shock and
Vibration Digest, 1998. 30(1): p. 4-13.
18. Laws, W. and A. Muszynska, Periodic and continuous vibration monitoring for
preventive/predictive maintenance of rotating machinery. Journal of Engineering for Gas
Turbines and Power, 1987. 109(2): p. 159-167.
19. Atoui, I., et al., Fault detection and diagnosis in rotating machinery by vibration monitoring
using FFT and Wavelet techniques. in Systems, Signal Processing and their Applications
(WoSSPA), 8th International Workshop, 2013.
20. Sinha, J.K., Health monitoring techniques for rotating machinery. 2002, Doctoral
dissertation, University of Wales, Swansea.
21. Al-Bedoor, B., et al., Experiments on the extraction of blade vibration signature from the
shaft torsional vibration signals. Journal of Quality in Maintenance Engineering, 2003.
9(2): p. 144-159.
22. Sinha, J.K. and A.R. Rao, Vibration based diagnosis of a centrifugal pump. Structural
Health Monitoring, 2006. 5(4): p. 325-332.
23. Al-Ghamd, A.M. and D. Mba, A comparative experimental study on the use of acoustic
emission and vibration analysis for bearing defect identification and estimation of defect
size. Mechanical Systems and Signal Processing, 2006. 20(7): p. 1537-1571.
24. Scheffer, C. and P. Girdhar, Practical machinery vibration analysis and predictive
maintenance. 2004: Elsevier, Oxford.
25. Zala, K.P., K.M. Srivastava and N.H. Pancholi, A Review on Analysis of Low Pressure
Stage of Steam Turbine Blade with FEA. International Journal for Scientific Research &
Development 2013. 1(10): p. 3
26. Abdelrhman, A.M., et al., Condition Monitoring of Blade in Turbomachinery: A Review.
Advances in Mechanical Engineering, 2014. 2014: p. 10.
27. ABB, P.G.I., Blade Vibration Testing Requires Specialized Approach. Power Engineering,
1995. 99(11): p. P102.
REFERENCES
221
28. Guai, Y.K., Turbine blade crack detection using vibration testing methods. 2009, Masters
dissertation, Universiti Teknologi Malaysia, Faculty of Mechanical Engineering, Malaysia.
29. Traces Ecrites News, Installation of steam turbine blades at Alstom Power Belfort. 2012;
Available from: http://www.tracesecritesnews.fr/wp-content/uploads/2012/07/Turbine-
%C3%A0-vapeur-au-montage-chez-Alstom-Power-Belfort.-300x225.jpg [Accessed on:
01/02/2015]
30. Leong, M.S. and L.M. Hee, Blades rubs and looseness detection in gas turbines -
operational field experience and laboratory study. Journal of Vibroengineering, 2013.
15(3): p. 1311-1321.
31. Hee, L.M., M.S. Leong, and K. Hui, Blade Faults Classification and Detection Methods:
Review. Advanced Materials Research, 2014. 845: p. 123-127.
32. Rao, J., A. Pathak, and A. Chawla, Blade life: a comparison by cumulative damage theories.
Journal of Engineering for Gas Turbines and Power, 2001. 123(4): p. 886-892.
33. Leong, M.S., Field Experiences of Gas Turbines Vibrations-A Review and Case Studies.
Journal of System Design and Dynamics, 2008. 2(1): p. 24-35.
34. Choi, Y.S., D.A. Gottfried, and S. Fleeter, Analysis of structural mistuning effects on bladed
disc vibrations including aerodynamic damping, International Compressor Engineering
Conference, 2004.
35. Petrov, E. and D. Ewins, Effects of mistuning on the forced response of bladed discs with
friction dampers, in Evaluation, Control and Prevention of High Cycle Fatigue in Gas
Turbine Engines for Land, Sea and Air Vehicles. Paper 38, p. 1-16. Meeting Proceedings
RTO-MP-AVT-121. 2005, Neuilly-sur-Seine, France.
36. Ewins, D.J., The effects of detuning upon the forced vibrations of bladed disks. Journal of
Sound and Vibration, 1969. 9(1): p. 65-79.
37. Bhat, M.M., V. Ramamurti, and C. Sujatha, Studies on the determination of natural
frequencies of industrial turbine blades. Journal of Sound and Vibration, 1996. 196(5): p.
681-703.
38. Lim, M.H. and M.S. Leong, Diagnosis for loose blades in gas turbines using wavelet
analysis. Journal of Engineering for Gas Turbines and Power, 2005. 127(2): p. 314-322.
39. Orłowski, Z. Diagnostics in the life of steam turbines. 2001: Wydawnictwa Naukowo-
Techniczne, WNT, Warszaw.
40. Lim, M.H. and M.S. Leong, Reconstruction of Vital Blade Signal from Unsteady Casing
Vibration. Advances in Mechanical Engineering, 2014. 2014: p. 9.
41. Rao, A.R. and B. Dutta. Non intrusive method of detecting turbine blade vibration in an
operating power plant. in Proceedings of ISMA. 2010.
42. Hahn, W. and J.K. Sinha, Vibration Behaviour of a Turbo-Generator Set, in Sinha J.K. (ed.)
Vibration Engineering and Technology of Machinery. 2015, p. 155-161, Springer, London.
REFERENCES
222
43. Vedeneev, V., M. Kolotnikov, and P. Makarov, Numerical analysis of compressor blade
flutter in modern gas turbine engines. 2013.
44. Mazur, Z., et al., Steam turbine blade failure analysis. Engineering Failure Analysis, 2008.
15(1–2): p. 129-141.
45. Leong, M.S. and L.M. Hee, Blades rubs and looseness detection in gas turbines -
operational field experience and laboratory study. Journal of Vibroengineering, 2013.
15(3): p. 1311-1321.
46. Barschdorff, D. and R. Korthauer. Aspects of failure diagnosis on rotating parts of
turbomachines using computer simulation and pattern recognition methods, Paper HI,
International Conference on Condition Monitoring. 1986, Brighton, United Kingdom,.
47. Olgac, N., U. Zalluhoglu, and A.S. Kammer, Concept paper: a novel perspective on
blade/casing rub problem in turbomachinery.n.d.
48. Batailly, A., M. Legrand, and C. Pierre. Influence of abradable coating wear mechanical
properties on rotor stator interaction. in ASME 2011 Turbo Expo: Turbine Technical
Conference and Exposition. American Society of Mechanical Engineers. 2011.
49. Padova, C., et al., Experimental results from controlled blade tip/shroud rubs at engine
speed. Journal of Turbomachinery, 2007. 129(4): p. 713-723.
50. Al-Badour, F., L. Cheded, and M. Sunar. New Approach for the Indirect Detection of Blade-
to-Stator Rubbing in Turbo-Machinery Using Wavelet Techniques. in ASME, 12th Biennial
Conference on Engineering Systems Design and Analysis. 2014. American Society of
Mechanical Engineers.
51. Abdelrhman, A.M., et al., Vibration analysis of multi stages rotor for blade faults diagnosis.
Advanced Materials Research, 2014. 845: p. 133-137.
52. Mba, D. and L. Hall, The transmission of acoustic emission across large-scale turbine
rotors. NDT & E International, 2002. 35(8): p. 529-539.
53. Szczepanik, R., et al., Application of Blade-Tip Sensors to Blade-Vibration Monitoring in
Gas Turbines. Thermal Power Plants. 2012.
54. Hou, J., B.J. Wicks, and R.A. Antoniou, An investigation of fatigue failures of turbine
blades in a gas turbine engine by mechanical analysis. Engineering Failure Analysis, 2002.
9(2): p. 201-211.
55. Homji, C.B.M. and G. Gabriles, Gas turbine blade failures, causes, avoidance, and
troubleshooting. ATM, Proceeding of the 27th Turbomachinery Sympossium. 1998.
56. Cowles, B., High cycle fatigue in aircraft gas turbines—an industry perspective.
International Journal of Fracture, 1989. 80(2-3): p. 147-163.
57. Al-Bedoor, B. Discussion of the Available Methods for Blade Vibration Measurement. in
ASME 2002 Pressure Vessels and Piping Conference. 2002. American Society of
Mechanical Engineers.
REFERENCES
223
58. Al-Bedoor, B., Blade vibration measurement in turbo-machinery: current status. The Shock
and vibration digest, 2002. 34(6): p. 455-461.
59. Russhard, P., The Rise and Fall of the Rotor Blade Strain Gauge, in Vibration Engineering
and Technology of Machinery, J.K. Sinha, Editor. 2015, Springer International Publishing.
p. 27-37.
60. Knappett, D. and J. Garcia, Blade tip timing and strain gauge correlation on compressor
blades. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of
Aerospace Engineering, 2008. 222(4): p. 497-506.
61. Rao, J., A. Pathak, and A. Chawla, Blade life: a comparison by cumulative damage theories.
Journal of Engineering for Gas Turbines and Power, 2001. 123(4): p. 886-892.
62. Szwedowicz, J., S.M. Senn, and R.S. Abhari, Optimum strain gage application to bladed
assemblies. journal of turbomachinery, 2002. 124(4): p. 606-613.
63. Heath, S. and M. Imregun, A survey of blade tip-timing measurement techniques for
turbomachinery vibration. Journal of Engineering for Gas Turbines and Power, 1998.
120(4): p. 784-791.
64. Lawson, C.P. and P.C. Ivey, Tubomachinery blade vibration amplitude measurement
through tip timing with capacitance tip clearance probes. Sensors and Actuators A:
Physical, 2005. 118(1): p. 14-24.
65. University of Cambridge, Strain gauge on specimen. n.d.; Available from:
http://www.doitpoms.ac.uk/tlplib/mechanical-testing/images/strain-gauge-close.jpg
[Accessed on: 01/02/2015].
66. Hahn, W. and J.K. Sinha. Comparative Study Between In-Situ Measured Vibration Data at
Bearing and BTT on a LP Turbine Last Stage Blades in a Steam Turbo-Generator Set. in
ASME 2013 Gas Turbine India Conference. 2013. American Society of Mechanical
Engineers.
67. Mandache, C., T. Mcelhinney, and M. Nezih. Aircraft Engine Blade Tip Monitoring Using
Pulsed Eddy Current Technology. in 4th International Symposium on NDT in Aerospace,
Augsburg, Germany. 2012.
68. von Flotow, A., M. Mercadal, and P. Tappert. Health monitoring and prognostics of blades
and disks with blade tip sensors. in Aerospace Conference Proceedings, 2000 IEEE. 2000.
IEEE.
69. Duan, F., et al. Research on Detecting Technology of Rotating Blade Vibration Performance
Parameters. in Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09.
International Conference on. 2009.
70. Pavel, P. and V. František, Contactless Diagnostics of Turbine Blade Vibration and
Damage. Journal of Physics: Conference Series, 2011. 305(1): p. 012116.
71. Witoś, M., High Sensitive Methods for Health Monitoring of Compressor Blades and
Fatigue Detection. The Scientific World Journal, 2013. 2013.
REFERENCES
224
72. García, I., et al., An Optical Fiber Bundle Sensor for Tip Clearance and Tip Timing
Measurements in a Turbine Rig. Sensors, 2013. 13(6): p. 7385-7398.
73. Dimitriadis, G., et al., Blade-tip timing measurement of synchronous vibrations of rotating
bladed assemblies. Mechanical Systems and Signal Processing, 2002. 16(4): p. 599-622.
74. Chao, L. and J. Dongxiang, Improved Blade Tip Timing in Blade Vibration Monitoring with
Torsional Vibration of the Rotor. Journal of Physics: Conference Series, 2012. 364(1): p.
012136.
75. Carrington, I.B., et al., A comparison of blade tip timing data analysis methods. Proceedings
of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2001.
215(5): p. 301-312.
76. Li, D., Z. Xiaoliang, and Z. Jiang, A high sensitivity inductive sensor for blade tip clearance
measurement. Smart Materials and Structures, 2014. 23(6): p. 065018.
77. Tamura, K., M. Ono, S. Torii, and K. Morimoto. (2014). Non-contact Vibration
Measurement of the Rotor Blades that Play a Pivotal Role in the Reliability of Gas
Turbines. Mitsubishi Heavy Industries Technical Review, 2014. 51(1), 10.
78. Janicki, G., et al., Turbine Blade Vibration Measurement Methods for Turbocharges.
American Journal of Sensor Technology, 2014. 2(2): p. 13-19.
79. Pickering, T.M., Methods for Validation of a Turbomachinery Rotor Blade Tip Timing
System. 2014, MSc dissertation, Virginia Tech, Virginia.
80. Eshleman, R.L., and F.M. Lewis. Torsional vibration in reciprocating and rotating
machines. Shock and Vibration Handbook. 1988: McGraw-Hill, New York.
81. Vance, J.M., Rotordynamics of turbomachinery. 1988: John Wiley & Sons.
82. Al-Bedoor, B., Dynamic model of coupled shaft torsional and blade bending deformations
in rotors. Computer Methods in Applied Mechanics and Engineering, 1999. 169(1–2): p.
177-190.
83. Yang, C.H. and S.C. Huang, The coupled vibration in a shaft‐disk‐blades system. Journal of
the Chinese institute of engineers, 2005. 28(1): p. 89-99.
84. Yang, C.-H. and S.-C. Huang, The influence of disk's flexibility on coupling vibration of
shaft–disk–blades systems. Journal of Sound and Vibration, 2007. 301(1): p. 1-17.
85. Huang, S. and K. Ho, Coupled Shaft-Torsion and Blade-Bending Vibrations of a Rotating
Shaft–Disk–Blade Unit. Journal of Engineering for Gas Turbines and Power, 1996. 118(1):
p. 100-106.
86. Al-Bedoor, B., Reduced-order nonlinear dynamic model of coupled shaft-torsional and
blade-bending vibrations in rotors. Journal of Engineering for Gas Turbines and Power,
2001. 123(1): p. 82-88.
REFERENCES
225
87. Lebold, M. S., J. P. Bednar, and M. W. Trethewey, Environmental effects on torsional
vibration feature health monitoring. In Topics in Modal Analysis II, Vol. 6, pp. 313-322.
2012: Springer, New York.
88. Sunar, M. and B. Al-Bedoor, Vibration measurement of rotating blades using a root
embedded PZT sensor. Shock and Vibration, 2008. 15(5): p. 517-541.
89. Maynard, K.P. and M. Trethewey, Blade and shaft crack detection using torsional vibration
measurements Part 1: Feasibility studies. Noise & Vibration Worldwide, 2000. 31(11): p.
9-15.
90. Maynard, K.P. and M. Trethewey, Blade and shaft crack detection using torsional vibration
measurements Part 2: Resampling to improve effective dynamic range. Noise & Vibration
Worldwide, 2001. 32(2): p. 23-26.
91. Maynard, K. and M. Trethewey, Blade and shaft crack detection using torsional vibration
measurements Part 3: Field application demonstrations. Noise & Vibration Worldwide,
2001. 32(11): p. 16-23.
92. Maynard, K. and M. Trethewey. Application of torsional vibration measurement to blade
and shaft crack detection in operating machinery. in Maintenance and Reliability
Conference. Gatlinburg, Tennessee, USA. 2001.
93. Maynard, K., et al. Gas turbine blade and disk crack detection using torsional vibration
monitoring: a feasibility study. in Proceedings of 14th International Congress and
Exhibition on Condition Monitoring and Diagnostic Engineering Management
(COMADEM), University of Manchester, UK. 2001.
94. Maynard, K.P., et al. Application of double resampling to shaft torsional vibration
measurement for the detection of blade natural frequencies. in Proceedings of the 54th
Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA.
2000.
95. Al-Bedoor, B., et al., Experiments on the extraction of blade vibration signature from the
shaft torsional vibration signals. Journal of Quality in Maintenance Engineering, 2003.
9(2): p. 144-159.
96. Maynard, K.P. and M. Trethewey. On the feasibility of blade crack detection through
torsional vibration measurements. in Proceedings of the 53 rd Meeting of the Society for
Machinery Failure Prevention Technology, Virginia Beach, Virginia. 1999.
97. Al-Bedoor, B., et al., Shaft lateral and torsional vibration responses to blade (s) random
vibration excitation. Arabian Journal for Science and Engineering, 2004. 29(1): p. 39-68.
98. Al-Bedoor, B., S. Aedwesi, and Y. Al-Nassar, Blades condition monitoring using shaft
torsional vibration signals. Journal of Quality in Maintenance Engineering, 2006. 12(3): p.
275-293.
99. Abdelrhman, A.M., et al., A review of vibration monitoring as a diagnostic tool for turbine
blade faults. Applied Mechanics and Materials, 2012. 229: p. 1459-1463.
REFERENCES
226
100. Sinha, J.K., et al. Vibration Investigation for Low Pressure Turbine Last Stage Blade Failure
in Steam Turbines of a Power Plant. in ASME Turbo Expo 2012: Turbine Technical
Conference and Exposition. American Society of Mechanical Engineers. 2012.
101. Mathioudakis, K., E. Loukis, and K.D. Papailiou, Casing Vibration and Gas Turbine
Operating Conditions. Journal of Engineering for Gas Turbines and Power, 1990. 112(4): p.
478-485.
102. Rao, A.R. and B. Dutta. Non intrusive method of detecting turbine blade vibration in an
operating power plant. in Proceedings of ISMA. 2010.
103. Rao, A.R. and B. Dutta, In situ detection of turbine blade vibration and prevention. Journal
of failure analysis and prevention, 2012. 12(5): p. 567-574.
104. Forbes, G.L. and R.B. Randall, Gas Turbine Casing Vibrations under Blade Pressure
Excitation. MFPT 2009, 2009.
105. Forbes, G. and R. Randall. Simulated gas turbine casing response to rotor blade pressure
excitation. in Proceedings of the 5th Australasian Congress on Applied Mechanics. 2007.
Engineers Australia.
106. Forbes, G.L. and R.B. Randall, Estimation of turbine blade natural frequencies from casing
pressure and vibration measurements. Mechanical Systems and Signal Processing, 2013.
36(2): p. 549-561.
107. Forbes, G.L. and R.B. Randall. Detection of a Blade Fault from Simulated Gas Turbine
Casing Response Measurements. in 4th European Workshop on Structural Health
Monitoring. 2008.
108. Goldman, P. and A. Muszynska. Application of full spectrum to rotating machinery
diagnostics. Orbit, 1999. 1: p. 17-21.
109. Lee, C.W. and Y.-S. Han. The directional Wigner distribution and its applications. Journal
of Sound and Vibration, 1998. 216(4): 585-600.
110. Doguer, T. and J. Strackeljian. Vibration analysis using time domain methods for the
detection of small roller bearing defects. in 8th International Conference on Vibrations in
Rotating Machines. 2009. Vienna, Austria.
111. Zhou, X. and D. Luo. Research of amplitude-frequency domain parameters analysis for
condition detection and fault diagnosis. Research Journal of Applied Sciences, Engineering
and Technology, 2012. 4(19): p. 3787-3790.
112. El Hachemi Benbouzid, M. A review of induction motors signature analysis as a medium
for faults detection. IEEE Transactions on Industrial Electronics, 2000. 47(5): p. 984-993.
113. Li, Y., F. Gu, G. Harris, A. Ball, N. Bennett, and K.Travis. The measurement of
instantaneous angular speed. Mechanical Systems and Signal Processing, 2005. 19: p. 786-
805.
REFERENCES
227
114. Arif, S.J., I. Imdudullah and S.H. Laskar. Instantaneous Angular Speed Measurement for
Low Speed Machines. Innovative Systems Design and Engineering, 2012. 3(5): p. 21-32.
115. Yu, Y., J. Yang, and P. Zhou. Fault diagnosis of a diesel engine by using the analysis of
instantaneous angular speed with a flexible model. International Journal of Vehicle Noise
and Vibration, 2011. 7(4): p. 365-385.
116. Liu, Y. and F. Zhang Sai. Measurement and diagnostic system for crankshaft of diesel
engine. in Computer Application and System Modeling (ICCASM), 2010 International
Conference on. 2010.
117. Charles, P., et al., Detecting the crankshaft torsional vibration of diesel engines for
combustion related diagnosis. Journal of Sound and Vibration, 2009. 321(3): p. 1171-1185.
118. Charles, P., et al., Application of novel polar representation method for monitoring minor
engine condition variations. Mechanical Systems and Signal Processing, 2010. 24(3): p.
841-843.
119. Yang, J.G., et al. Fault detection in a diesel engine by analysing the instantaneous angular
speed. Mechanical Systems and Signal Processing, 2001. 15(3): p. 549-564.
120. Stander, C.J. and P.S. Heyns. Instantaneous angular speed monitoring of gearboxes under
non-cyclic stationary load conditions. Mechanical Systems and Signal Processing, 2005.
19(4): p. 817-835.
121. Wu, J.-D., et al. An expert system for the diagnosis of faults in rotating machinery using
adaptive order-tracking algorithm. Expert Systems with Applications, 2009. 36(3): 5424-
5431.
122. Scheffer, C. and P. Girdhar. Practical Machinery Vibration Analysis and Predictive
Maintenance, 1st Edition. 2004. Elsevier: Oxford.
123. DassaultSystèmesSimuliaCorp.ABAQUS6.10:Analysisuser’smanual.2010.
124. Rao, A. R., In-Situ and Non-Intrusive Measurement Technique for Detection of Turbine
Blade Vibrations. BARC Newsletter, 2005. 261: 7.
125. ABSSAC Ltd., Helical shaft couplings [Catalogue]. n.d.; Available from:
http://www.abssac.co.uk [Accessed on: 01/02/2015].
126. Ewins, D. J. (2000). Modal testing: theory, practice and application, Vol. 2. 2000: Research
Studies Press, Baldock.
127. Gubran, A.A. and J.K. Sinha, Comparison of On-Bearing and On-Casing Vibration for
Blade Health Monitoring in Rotating Machine, in Vibration Engineering and Technology of
Machinery, J.K. Sinha, Editor. 2015, Springer International Publishing. p. 107-118.
128. Gubran, A.A. and J.K. Sinha. Comparison between long and short blade vibration using
shaft instantaneous angular speed in rotating machine. in ASME Turbo Expo 2014: Turbine
Technical Conference and Exposition. 2014. American Society of Mechanical Engineers.
REFERENCES
228
129. Gubran, A.A. and J.K. Sinha, A Comparison of On-Bearing and Shaft Torsional Vibration
for Blade Vibration. SOP Transactions on Signal Processing, 2014. 1(1): p. 1-9.
130. Gubran, A.A. and J.K. Sinha. Blade Vibration: A comparison of On-bearing and Shaft
Torsional Vibration. in Proc. of International Conference on Vibration Problems (ICOVP
2013). 2013.
131. Gubran, A.A. and J.K. Sinha, Shaft instantaneous angular speed for blade vibration in
rotating machine. Mechanical Systems and Signal Processing, 2014. 44(1–2): p. 47-59.