aus

ulo-265

VibrationCondition monitoringGearbox

f aocesenrimasiso exnito

highlighted. The differences in the parameters evolution of each NDT technique are discussed and the

ains inan leas the inon-linechniquh effor

250 h) and correlated special features (based on higher order mo-ments) extracted form the vibration recordings with the Fe debrismass accumulated during the tests. They have integrated their re-sults in a fuzzy logic based health monitoring system with satisfac-tory performance. Researchers in the eld have focused mainly onadvanced signal processing techniques applied on vibration record-ings coming mainly from articial gear defects in short tests ratherthan inducing gear pitting damage in multi-hour testing.

the power spectrum distribution. The same authors have also ap-plied the WignerVille distribution [12] as well as the wavelettransform [13] on vibration and acoustic signals for the samepurpose.

The interest for applications of acoustic emission (AE) for condi-tion monitoring in rotating machinery is relatively new and hasgrown signicantly over the last decade. AE in rotating machineryis dened as elastic waves generated by the interaction of twomedia in motion, i.e. a pair of gears. Sources of AE in rotatingmachinery include asperities contact, cyclic fatigue, friction, mate-rial loss, cavitations, leakage, etc. AE technique has drawn

* Corresponding author.

Applied Acoustics 70 (2009) 11481159

Contents lists availab

A

l seE-mail address: kostopoulos@mech.upatras.gr (V. Kostopoulos).Few research teams have published experimental data coming fromlong-term testing to see the effect of natural gear pitting mostlyupon vibration recordings. Dempsey et al. [14] and Decker withLewicki [5] have conducted some excellent experimental work atGRC/NASA and published interesting results from extensive geartesting at a special test-rig utilizing vibration and oil debris mea-surements. With the clear goal to improve the performance of thecurrent helicopter gearbox health monitoring systems, they havetested gears at high shaft speed for multi-hour periods (up to

features of vibration signals and showed that unlike the time-fre-quency distribution, which incorporates a constant time and fre-quency resolution, the wavelet transform can accommodatesimultaneously both the large and small scales in a signal, en-abling the detection of both distributed and local faults. Baydarand Ball [10,11] have proposed the instantaneous power spectrumand have shown that it is capable in detecting local tooth faults instandard industrial helical gearboxes. The propagation of localfaults was identied by monitoring variations in the features of1. Introduction

In gearboxes and power drive trdetection is often very critical and caviation and in industry as well. Thunon-destructive inspection and/orgrowing and effective diagnostic teare the objective of extensive researc0003-682X/$ - see front matter 2009 Elsevier Ltd. Adoi:10.1016/j.apacoust.2009.04.007superiority of AE over vibration recordings for the early diagnosis of natural wear in gear systems isconcluded.

2009 Elsevier Ltd. All rights reserved.

general, gear damaged to increased safety interest for their periodichealth monitoring ises and methodologiests over the last 50 years.

The publications in the eld of condition monitoring via vibra-tions are quite versatile. Selecting a few and focusing on advancedsignal processing techniques the works of Wang and McFadden[6,7] must be mentioned, that utilized time-frequency analysistechniques and showed that the spectrogram has advantages overWignerVille distribution for the analysis of vibration signals forthe early detection of damage in gears. The same authors havealso employed the wavelet transform [8,9] to analyze the localAdvanced signal processingAcoustic emission posed utilizing the discrete wavelet transform. The evolution of selected parameters/features versus

test time is provided, evaluated and the parameters with the most interesting diagnostic behaviour areCondition monitoring of a single-stage gecracks utilizing on-line vibration and aco

T.H. Loutas, G. Sotiriades, I. Kalaitzoglou, V. KostopoDepartment of Mechanical Engineering and Aeronautics, University of Patras, Patras GR

a r t i c l e i n f o

Article history:Received 7 October 2008Received in revised form 15 April 2009Accepted 16 April 2009Available online 17 May 2009

Keywords:

a b s t r a c t

The condition monitoring omethodologies and the prniques is the aim of the prefor this purpose. The expepresented in detail. Emphemission signals in order tnostic value from the mo

Applied

journal homepage: www.ell rights reserved.rbox with articially induced geartic emission measurements

s *

00, Greece

lab-scale, single stage, gearbox using different non-destructive inspectionssing of the acquired waveforms with advanced signal processing tech-t work. Acoustic emission (AE) and vibration measurements were utilizedental setup and the instrumentation of each monitoring methodology areis given on the signal processing of the acquired vibration and acoustictract conventional as well as novel parametersfeatures of potential diag-red waveforms. Innovative wavelet-based parametersfeatures are pro-

le at ScienceDirect

coustics

vier .com/locate /apacoust

attention as it offers some advantages over classical vibration mon-itoring. First of all, as AE is a non-directional technique, one AE sen-sor is sufcient in contrast to vibration monitoring which mayrequire information from three axes. Since AE is produced atmicroscopic level it is highly sensitive and offers opportunitiesfor identifying defects at an earlier stage when compared to othercondition monitoring techniques. As AE mainly detects high-fre-quency elastic waves, it is not affected by structural resonancesand typical mechanical background noise (under 20 kHz). Tandonand Mata [14] applied AE to spur gears in a gearbox test-rig. Theysimulated pits of constant depth but variable size and AE parame-ters such as energy, amplitude and counts were monitored duringthe test. AE was proved superior over vibration data on early detec-tion of small defects in gears. Singh et al. [15] also applied AE tech-nique in condition monitoring of test-rig gearboxes, whilevibration methods was also used for comparative purposes byplacing accelerometers on the gearbox casing. They also concludedthat AE provided early damage detection over vibration monitor-ing. Toutountzakis et al. [16] investigated the inuence of oil tem-perature and of the oil lm thickness on AE activity and on AEsignals captured during continuous running of a back-to-backgearbox test-rig. It was observed that the AE RMS varied with timeas the gear box reached a stabilized temperature and the variationin AE activity RMS could be as much as 33%.

Tan and Mba [17] discussed in more detail the oil temperatureeffect on AE and concluded that the source of AE mechanism thatproduced the gear mesh bursts was from asperities contact. Tout-ountzakis and Mba [18] presented some interesting observationson AE activity due to misalignment and natural pitting and con-cluded that the AE technique is applicable for monitoring geardamage. Finally a comparative study [19] between AE and vibra-tions was conducted to show the diagnostic and prognostic capa-bilities of each technique in several multi-day tests in a single-stage gearbox.

The present work reports the results concluded by long term(50 h) experiments to a defected gear system, with a transversecut of 25% of root thickness to simulate the tooth crack. Differentparameters, resulted by the analysis of the recording signals (bothcoming from vibration monitoring and AE) are presented andtheir diagnostic value is discussed in the direction of being used

D

d

W

Table 1Conventional parameters calculated from the acquired waveforms.

Time domain parameters Frequency domain parameters

p1 PN

n1xnN p12

PKk1skK

p2 PN

n1 xnp12

N1

rp13

PKk1 skp12

2

K1

p3 PN

n1jxnj

pN

2p14

PKk1 skp12

3

Kp13

p 3

p4 PN

n1 xn2

N

rp15

PKk1 skp12

4

Kp213

p5 max jxnj p16 PK

k1 fkskPKk1sk

p6 PN

n1xnp13

N1p32p17

PKk1fkp16

2skK

r

p7 PN

n1xnp14

N1p42p18

PKk1 f

2kskPK

k1sk

s

p8 p5p4 p19 PK

k1 f4kskPK

k1 f2ksk

s

p9 p5p3 p20 PK

k1 f2k skPK

k1skPK

k1 f4ksk

qp10 p4

1N

PNn1 jxnj

p21 p17p16p11 p5

1N

PNn1 jxnj

p22 PK

k1 fkp163sk

Kp317

p23 PK

k1 fkp164sk

Kp417

p24 PK

k1 fkp161=2sk

Kp17

p

T.H. Loutas et al. / Applied Acoustics 70 (2009) 11481159 1149Fig. 1. Test bench setup.

Time synchronous

averaging (only for vibration

signals)

Vibration and AE signalsFig. 2. Flow chart of the DWiscrete Wavelet transform n levels of

ecomposition

Energy content determination for each level

Plot of energy levels vs

defect types

avelet type

Number of levels T-based methodology.

for the development of a condition monitoring system. Further-more, a systematic comparison of the different diagnostic param-eters is provided, in order to assess which are the most robustand reliable ones for the condition monitoring of gearboxes anddrive trains. The paper closes with the conclusions drawn fromthis study.

2. Experimental setup

Fig. 1 shows the experimental setup used for the gears testing.The test-rig consists of two gears made from 045M15 steel with amodule of 3 mm, pressure angle 20, which have 53 and 25 teethwith 7 mm face width. The axes of the gears are supported bytwo ball bearings each. The entire system is settled in an oil basinin order to ensure proper lubrication. The gear box is powered by amotor and consumes its power on a generator. Their characteristicsare as follows:

1 stage gearbox with two gears (25 and 53 teeth); 3-phase 5 hp motor (220 V, 9 A, 50 Hz, 1400 rpm) con-

trolled by inverter; single phase generator with continuous power consump-

tion control (load uctuation), 4.2 KVA, 3000 rpm, 50 Hz; the oil pump is of the wet type without oil recirculation; the shafts are ball bearing supported.

Two non-destructive techniques have been employed to moni-tor the gearbox during operation, namely vibration and acousticemission. Two Bruel & Kjaer accelerometers were used for thevibration monitoring both mounted upon the gearbox case, onein each side-axis. The sampling frequency used was 50 kHz andsignals of 1 s duration were recorded. Additionally three wide bandFig. 3. Tooth crack.

0 25 300 350 400 450 500 550 600 650

0.0

0.2

0.4

0.6

0.8

1.0 (tooth break)

700%

1500%

2nd transition

3rd transition

1st transition

ED1

# of recordings

0.4

0.6

0.8

1.0

aram

ete

r

0.4

0.6

0.8

1.0

par

ame

ter

(a) (b)

(c)

1150 T.H. Loutas et al. / Applied Acoustics 70 (2009) 114811590 25 400 450 500 550 600 650

0.0

0.2 450%

2800%

p7# of recordings

Fig. 4. Parameters evolution during the test for vib0 25 400 450 500 550 600 650

0.0

0.2350%

1100%

p6 p

# of recordings

0 25 400 450 500 550 600 650

0.0

0.2

0.4

0.6

0.8

1.0

300%

90%

p13

para

mete

r

(d) # of recordings

ration ch1 (a) ED1, (b) p6, (c) p7 and (d) p13.

acoustic emission sensors manufactured by Physical Acoustics Cor-poration (PAC) with a frequency response range of 100800 kHzrecorded continuous AE signals of 100 ms duration at a samplingrate of 2 MHz. Fig. 1 shows the positions of all the sensors.

One AE sensor is mounted on the output shaft (AE channel 1),the second is placed upon one of the bearings of the same shaft(AE channel 3) and the third (AE channel 2) is in friction contactwith the input rotating gear. A special innovative device was de-signed in-house and discussed elsewhere [20] in order to mountthe AE sensor upon a rotating component without the expensive/demanding solution of the slip-ring generally used in literature.

The recordings of all the above data coming from accelerome-ters and AE sensors are realized by a National Instruments NI-6070 1MS/SEC FIREWIRE data acquisition device and assisted byspecial software developed in-house, in Labview programmingenvironment. Finally, the temperature of the oil bath within thegearbox is measured via a thermocouple.

3. Signal processing methodologies

Signicant effort was dedicated to the signal processing of thevibration and AE waveforms acquired during the tests. The goalset a priori was to calculate a number of parametersfeatures ex-tracted by the signals and check their behavior during the tests

in order to identify the most promising ones that may be usedfor damage detection and condition monitoring of the gear system.In the literature, very few research groups have been involved inlong term gear testing and they have mainly used higher order mo-ments and their combinations to form diagnostic parameters [15]with interesting behavior during the tests. In this work, apart fromparameters usually found in the literature, we have introducedsome more advanced signal processing techniques such as the dis-crete wavelet transform and extracted innovative wavelet-basedparameters from the signals. In total more than 40 parametersare checked for their diagnostic ability. Those capable of monitor-ing the damage are identied and compared.

3.1. Conventional parameters

In Table 1 conventional parameters from the time and fre-quency domain that were calculated, are shown. Where x(n) is asignal series for n = 1, 2, . . . , N, N is the number of signal samplesand s(k) is the Fourier transform for k = 1, 2, . . . , K, K is the numberof spectrum lines, fk is the frequency value of the kth spectrum line.Parameter p1 is the mean of the signal, p2 is its root mean square, p5is obviously the absolute maximum of the signal, p4 the standarddeviation, p6 and p7 the third and fourth moments whilst p8p11 re-sult as a combination of previous parameters all calculated by the

0.006

0.008

0.010

0.012

0.014

25%

1st transition

ED1

0.0005

0.0010

0.0015

0.0020

2200%

1st transition

p6 p

aram

ete

r

(a) (b)

T.H. Loutas et al. / Applied Acoustics 70 (2009) 11481159 11510 50 100 150 200 250 300 350 4000.000

0.002

0.004

# of recordings

0 50 100 150 200 250 300 350 4000.00000

0.00005

0.00010

0.00015

122%

1st transition

p7 p

aram

ete

r

(c) # of recordings

Fig. 5. A magnication of Fig. 2 o0 50 100 150 200 250 300 350 400

0.0000

# of recordings

0 50 100 150 200 250 300 350 4000.04

0.06

0.08

0.10

p13

para

mete

r

(d) # of recordings

ver the rst 420 recordings.

signal in time domain. Correspondingly, p12p24 are extracted inthe frequency domain.

These parameters are typical parameters in the time and in thefrequency domain that can be extracted from any signal.

3.2. Discrete wavelet transform based parameters

The wavelet transform was utilized to develop new parametersand check their behavior during the tests. The major advantage ofwavelets is their inherent ability to perform local analysis withvarying precision. Wavelet transform treats low frequencies withlow resolution and high frequencies with high resolution [21].Wavelets stem from the iteration of lters and lter banks (withrescaling) so they are inherently orthogonal or biorthogonal. Incontrast to the Fourier analysis, which consists of breaking up asignal into sine waves of various frequencies, wavelet analysisbreaks up the signal into shifted and scaled versions of the original(or mother) wavelet.

The inverse discrete wavelet transform can be expressed as:

f t cXj

Xk

DWj; kwj;kt 1

where c is a constant depending only on w. Eq. (1) is the backboneof the present work and the whole philosophy of using waveletsfor analysis of transient and non-stationary signals, as it statesthat a given time series signal can be decomposed by the discrete

wavelet transform into its wavelet levels, where the summation ofthese levels represent the original input signal. The decomposedwavelet levels are channeled in such a way that each level corre-sponds to a certain frequency range of the acquired signal. TheDWT-based methodology used in this work was introduced anddescribed elsewhere [21,22]. Fig. 2 schematically summarizes thecomplete procedure. It comprises the Discrete Wavelet Transform(DWT) of the time synchronous averaged acquired vibration sig-nals and AE signals in 10 levels of decomposition using thedb10 wavelet. As far as the type of wavelet for the discrete trans-form is concerned db10 was a good compromise of smooth func-tion, without sharp edges as in the case of db wavelets of lowerorder.

The family of Daubechies wavelets was chosen because it con-sists of biorthogonal, compactly supported wavelets, satisfactorilyregular though not symmetrical. Other wavelets having similarproperties to the Daubechies family, such as symlets or coietswere also tried with minor impact upon the results. The decom-posed wavelet levels are split in a way that each level correspondsto a certain frequency range. After the 10-level decomposition theenergy of each level (10 details and one approximation) is calcu-lated. Thus eleven parameters namely ED1ED10 (for the details)and Ea10 (for the approximation) were resulted.

Additionally the sub-band wavelet entropy (SWE) is calculated.SWE is dened in terms of the relative wavelet energy of the wave-let coefcients. The energy at each resolution level j is dened in

0.4

0.6

0.8

1.0

D1 0.4

0.6

0.8

1.0

ED2

(a) (b)

1152 T.H. Loutas et al. / Applied Acoustics 70 (2009) 114811590 25 400 450 500 550 600 650

0.0

0.2

E

# of recordings

(c)

0 25 400 450 500 550 600 650

0.0

0.2

0.4

0.6

0.8

1.0

p12

para

met

er# of recordings

Fig. 6. Parameters evolution during the test for vibr0 25 400 450 500 550 600 650

0.0

0.2

# of recordings

(d)

0 25 400 450 500 550 600 650

0.0

0.2

0.4

0.6

0.8

1.0

p23

para

mete

r# of recordings

ation ch2 (a) ED1, (b) ED2, (c) p12 and (d) p23.

(1). The total energy of the wavelet coefcients will then be givenby:

Etotal Xj

Ej 2

Then; the normalized values are expressed as : pj Ej=Etotal:3

and the SWE at resolution j is defined as : Hj pj log pj4

Eleven more parameters HD1HD10 and HA1 are then calculated.

4. Test procedure and results

The experimental setup was analytically described in Section 2of the present work. Many tests were conducted in order to cali-brate the multi-sensor conguration and assure the repeatabilityof the recordings and the proper operation with minimum noiseof acquisition cards, ampliers, pre-ampliers as well as the vari-ous cables and connections.

Results in terms of various parameters evolution during thetest from a representative test on a gear system with a transversecut of 25% of root thickness to simulate the tooth crack (Fig. 3) willbe presented and detailed in this study. Two more tests were con-

ducted on the same conguration yielding similar parameterbehaviours. Recordings every 5 min were acquired and a total of650 recordings (54 h of test duration) were resulted until the ter-mination of the test, that is about 2 h after the tooth was cut-offthe gear. This type of test was preferred in order to have the oppor-tunity to monitor both damage modes i.e. the natural gear wear aswell as the crack propagation, though the latter is dominant be-tween the two as seen by the minimumwear in the gear faces afterthe tests. From the recorded vibration and AE waveforms thewhole set of parametersfeatures as described in Section 3 are calculated utilizing in-house algorithms developed in Matlabenvironment. In the following sections the behaviour of the best from a diagnostic point of view parameters is analyticallypresented.

4.1. Vibration results

From a total set of about 50 parameters, about 12 of them seemto have a clear diagnostic potential. For the vibration recordings,parameters p4, p6, p7, p12, p13, p17, p21, p24, ED1, ED2 and ED3proved capable of attending the damage accumulation upon thegears and have shown an almost monotonic behaviour duringthe tests. It is reminded here that p4, p6 and p7 parameters comefrom the time domain, parameters p12, p13, p17, p21 and p24come from the frequency domain whereas ED1, ED2 and ED3 arethe wavelet-based ones. Fig. 4 depicts the evolution of four se-

0.04

0.061st transition

1

0.06

0.08

0.10

oil temeperature effect

2

(a) (b)

T.H. Loutas et al. / Applied Acoustics 70 (2009) 11481159 11530 50 100 150 200 250 300 350 4000.00

0.02

ED

# of recordings

0 50 100 150 200 250 300 350 4000.20

0.25

0.30

0.35

0.40

0.45

0.50

p12

para

met

er

(c) # of recordings

Fig. 7. A magnication of Fig. 4 o0 50 100 150 200 250 300 350 4000.00

0.02

0.04ED

# of recordings

0 50 100 150 200 250 300 350 4000.00

0.01

0.02

0.03

0.04

p23

para

mete

r

(d) # of recordings

ver the rst 420 recordings.

lected parameters for ch1 during the test namely ED1, p6, p7 andp13. All parameters shown are normalized in the (01) range. Atrst sight it seems that no important changes take place untilapproximately the 420th recording (35 h).

To assist the more accurate observation of the parametersevolution during this stage of the test, a magnication was drawnin the diagrams of Fig. 5. In Fig. 5ac a transition in the regionnear 325th recording (27 h), in the middle of the test, is ob-served. The size of this rst (of the three transitions observedthroughout the test) transition depends on the parameter oneis looking at and in the case of p6 parameter reaches 2200%. Priorto this point no signicant variation of the parameters are ob-served. This transition is not evident in Fig. 5d though. A 2ndtransition is identied at the region of the 530th recording(44 h) according to Fig. 4ad. The percentage rise reaches2800% for parameter p7. A 3rd transition related to the toothcut-off takes place at about the 625th recording (52 h) asall the graphs in Fig. 4 clearly show. Parameter ED1 sees an in-crease of 700%.

These transitions are important and possess diagnostic value asthey can be used to dene and characterize critical stages of thegears damage accumulation and evolution.

Results from the processing of the vibration signals from ch2are depicted in Fig. 6. The 3rd transition at approximately the

625th recording (52 h) is quite clear whereas the 2nd is not clearin any of the selected parameters. Looking at Fig. 7 the location ofthe 1st transition is not very clear as well, at least not as evident asin the case of vibration ch1, rendering ch2 less interesting diagnos-tically. Parameter ED1 presented in Fig. 6a suggests the 1st transi-tion at the neighbourhood of the 325th recording (27 h). In spiteof the uncertainty and the signicant uctuations, the aboveparameters are monotonically increased, which is very useful froma diagnostic point of view.

The area at the very beginning of the test highlighted in Fig. 7bseems to have non-consistent parameter values behaviour, a phe-nomenon that is attributed to the oil temperature effect upon therecordings. In the beginning of the test it normally takes few hoursuntil the lubricant reaches a steady temperature. While the oiltemperature changes, so does the oil lm thickness between theasperity contacts of the gears affecting the vibration as well asthe AE recordings. This is a statement not only valid for the param-eter ED2 of Fig. 7b. This behaviour in the beginning of the tests isobserved more or less in almost every parameter presented inthe paper.

In ch3, a behaviour similar to that of vibration ch1 is observed.The 2nd and 3rd transitions are clearly dened in the graphs givenin Fig. 8. The 1st transition is not as clear as in Fig. 4 graphs but stillcan be marked at least in Fig. 9a and b.

0.2

0.4

0.6

0.8

1.0

(tooth break)

440%

3rd transition

2nd transition

ED1

para

mete

r

0.2

0.4

0.6

0.8

1.0

320%

ED2

para

mete

r

(a) (b)

1154 T.H. Loutas et al. / Applied Acoustics 70 (2009) 114811590 25 400 450 500 550 600 650

0.0600%

# of recordings

0 25 400 450 500 550 600 650

0.0

0.2

0.4

0.6

0.8

1.0

690%

1500%

p7 p

aram

eter

(c) # of recordings

Fig. 8. Parameters evolution during the test for vib0 25 400 450 500 550 600 650

0.0700%

# of recordings

0 25 400 450 500 550 600 650

0.0

0.2

0.4

0.6

0.8

1.0

130%

110%

p12

para

mete

r

(d) # of recordings

ration ch3 (a) ED1, (b) ED2, (c) p7 and (d) p12.

cous0.010

0.015

1st transition

eter

(a)

T.H. Loutas et al. / Applied ATable 2 summarizes the percentage changes of the variousparameters shown in vibration channel 3. Channel 2 is not in-cluded as the least interesting from the three. The 1st transitionin some cases is not clear and no value is given.

4.2. AE results

For the acoustic emission recordings, parameters p4, p6, p7, p12,p13, p17, p21, p24, ED3, ED4 and ED5 proved capable of attendingthe damage accumulation upon the gears and have shown an inter-

0 50 100 150 200 250 300 350 4000.000

0.005ED1

para

m

# of recordings

0 50 100 150 200 250 300 350 4000.0000

0.0002

0.0004

0.0006

0.0008

p7 p

aram

ete

r

# of recordings

(c)

Fig. 9. A magnication of Fig. 6 o

Table 2Parameter percentage changes for vibration channels 1 and 3.

Channel Parameter 1st transition (%) 2nd transition (%) 3rd transition (%)

1 ED1 25 1500 700p6 2200 1100 350p7 122 2800 450p13 90 300

3 ED1 600 440ED2 700 320p7 1500 690p12 130 1100 50 100 150 200 250 300 350 4000.00

0.02

0.04

0.06

0.08

1st transition

ED2

para

mete

r

# of recordings

0.20

(b)

(d)

tics 70 (2009) 11481159 1155esting monotonic behaviour during the tests. Parameters p4, p6and p7 are calculated in the time domain, p12, p13, p17, p21 andp24 in the frequency domain and parameters ED3, ED4, ED5 arethe wavelet-based ones. AE in total has a very interesting behav-iour in this test (Fig. 10). Unlike the results from vibration record-ings in the previous section, even from the early beginning it has aseemingly linear increasing behaviour and it seems capable ofdiagnosing even the initial stages of crack propagation. A closerlook at the rst 400 recordings reveals a bilinear behaviour moreevident in parameters ED3 and ED5 (see Fig. 11).

It is reminded that the dominant damage mode involved in thistest is the crack propagation and much less the natural wear. Thischange in the slope could be associated with changes in the crackpropagation rate. Parameter ED5 has a diagnostic advantage sinceits slope changes close to the 150th (12.5 h) recording much ear-lier than the 250th recording (21 h) of parameter ED3. An impor-tant transition around the 625th recording (52 h) warns withrespect to the oncoming tooth failure.

In acoustic emission ch2, interesting diagnostically behavioursare acquired as Fig. 12 shows. More than two different slopes canbe identied as Fig. 13 suggests for parameters ED3 and ED4. Inany case, as in AE ch1, an important transition around the 625threcording (52 h) warns with respect to the oncoming toothfailure.

0 50 100 150 200 250 300 350 4000.05

0.10

0.15p1

2 pa

ram

ete

r

# of recordings

ver the rst 420 recordings.

b)cous1.0(a) (1156 T.H. Loutas et al. / Applied AAE results of diagnostic parameters coming from ch3 (Fig. 14)seems to have an almost linear behaviour again, with not signi-cant slope changes during the test, thus making ch3 behaviourthe least interesting among the three AE channels. Still the criticaltransition at around the 625th recording (52 h) is clearly shown.

0 100 200 300 400 500 6000.0

0.2

0.4

0.6

0.8

transition(tooth break)90%

300%

ED3

para

mete

r

# of recordings

ED5

para

mete

r

0 100 200 300 400 500 6000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

45%

60%

p4 p

aram

ete

r

# of recordings

(d(c)

Fig. 10. Parameters evolution during the test for

0 50 100 150 200 250 300 350 4000.05

0.10

0.15

0.20

0.25

ED3

para

mete

r

# of recordings

(a) (

Fig. 11. A magnication of ED 3 and ED 51.0 tics 70 (2009) 11481159Table 3 summarizes the percentage changes of the various param-eters presented in all three AE channels. The rst refers to thechange observed from the test start until the transition and thesecond refers to the change measured at the neighbourhood ofthe transition.

0 100 200 300 400 500 6000.0

0.2

0.4

0.6

0.8

1700%

130%

# of recordings

0 100 200 300 400 500 6000.4

0.5

0.6

0.7

0.8

0.9

1.0

30%

50%p12

para

mete

r

# of recordings

)

AE ch1 (a) ED3, (b) ED5, (c) p4 and (d) p12.

0 50 100 150 200 250 300 350 4000.00

0.05

0.10

0.15

0.20

0.25

ED5

para

met

er

# of recordings

b)

over the rst 420 recordings AE ch1.

cousT.H. Loutas et al. / Applied AAfter analysing and commenting on the behaviour of carefullyselected parameters in the previous, in Fig. 15 an example of the

0 100 200 300 400 500 6000.0

0.2

0.4

0.6

0.8

1.0

35%

100%

ED3

para

mete

r

# of recordings

0 100 200 300 400 500 600

0.0

0.2

0.4

0.6

0.8

1.0

3500%

400%

p6 p

aram

eter

# of recordings

(a) (b

(d(c)

Fig. 12. Parameters evolution during the test for

0 50 100 150 200 250 300 350 400 450 5000.00

0.05

0.10

0.15

0.20

0.25

0.30oil temperature effect

ED3

para

mete

r

# of recordings

(a) (

Fig. 13. A magnication over the rst 500 rectics 70 (2009) 11481159 1157behaviour of non-useful diagnostically- parameters extracted bythe analysis of AE monitored signals is depicted.

0 100 200 300 400 500 6000.0

0.2

0.4

0.6

0.8

1.0

290%

80%

ED4

para

mete

r

# of recordings

0 100 200 300 400 500 6000.0

0.2

0.4

0.6

0.8

1.0

60%

150%p13

para

met

er

# of recordings

)

)

AE ch2. (a) ED3, (b) ED5, (c) p6 and (d) p13.

0 50 100 150 200 250 300 350 400 450 5000.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

ED4

para

mete

r

# of recordings

b)

ordings for (a) ED3 and (b) ED4 AE ch2.

(cous0.8

0.9

1.0ete

r

(a) 1158 T.H. Loutas et al. / Applied A5. Conclusions

The health monitoring of rotating machinery and power drivetrains is of utmost importance in various industrial applicationsin industry and in rotorcraft aviation. A single-stage gearbox wasutilized in order to study the development of damage in articiallyinduced cracks in the gears. Multi-hour tests were conducted andnumerous recordings were acquired using acoustic emission andvibration monitoring. The main goal of the study was to extract aset of parametersfeatures and check their diagnostic behaviour

0 100 200 300 400 500 600

0.05

0.10980%

25%

ED5

para

m

# of recordings

0 100 200 300 400 500 6000.2

0.4

0.6

0.8

1.0

55%

51%

p4 p

aram

eter

# of recordings

((c)

Fig. 14. Parameters evolution during the test for

Table 3Parameter percentage changes for AE channels 13.

Channel Parameter Start until transition (%) Transition (%)

1 ED3 300 90ED5 1700 130p4 60 45p12 50 30

2 ED3 100 35ED4 290 80p6 3500 400p13 150 60

3 ED5 25 980ED8 350 870p4 55 51p13 170 1300 100 200 300 400 500 6000.00

0.05

0.10

0.8

0.9

1.0

870%

350%

ED8

para

mete

r

# of recordings

0.8

1.0

b)

d)

tics 70 (2009) 11481159searching for the most potential and appropriate for future healthmonitoring schemes. A large number of parameters are proposed.Among them, conventional time domain based parameters, fre-quency domain based and a set of innovative parameters basedon the discrete wavelet transform.

Detailed results on the diagnostic behaviour and potentiality ofthe most interesting of the above parameters/features novel andconventional were analytically presented and discussed. Transi-tions in the parameter values were highlighted suggesting criticalchanges in the operation of the gearbox. Very interesting behaviourof selected parameters was observed for both monitoringtechniques.

The oil temperature effect upon vibration and AE recordingswas clearly identied in the beginning of the tests rendering itan important factor that should be taken into account in healthmonitoring of rotating structures. Several features extracted fromthe recorded vibration and AE waveforms revealed their variationas the oil temperature was rising up to the operationaltemperature.

Acoustic emission technique seems superior in the early stagesof the test and up to the middle being more capable of giving sig-nicant indications and differentiations to the monitored parame-ters, something that was not observed for the vibrationmonitoring. A regionally linear behaviour of AE parameters wasobserved and the gradients changes were associated with changesin the crack propagation rate. A superiority of the AE technique

0 100 200 300 400 500 6000.0

0.2

0.4

0.6130%

170%

p13

para

mete

r

# of recordings

AE ch3. (a) ED5, (b) ED8, (c) p4 and (d) p13.

0.5

0.6

0.7

0.8

0.9

1.0p8

par

amete

r

0.2

0.4

0.6

0.8

1.0

ED8

para

mete

r

(a) (b)

tes

T.H. Loutas et al. / Applied Acoustics 70 (2009) 11481159 1159over vibration monitoring regarding the monitoring of early crackpropagation, is thus concluded.

Acknowledgements

A big part of this research work was conducted in the frame-work of FP6-European Project ADHER Automated Diagnosis forHelicopter Engines and Rotating Parts Project.

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Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurementsIntroductionExperimental setupSignal processing methodologiesConventional parametersDiscrete wavelet transform based parameters

Test procedure and resultsVibration resultsAE results

ConclusionsAcknowledgementsReferences