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DEFECT DETECTION IN PIPELINE USING ACOUSTIC EMISSION
AHMED KADHIM ZARZOOR
UNIVERSITI TEKNOLOGI MALAYSIA
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4
DEFECT DETECTION IN PIPELINE USING ACOUSTIC EMISSION
AHMED KADHIM ZARZOOR
A project report submitted in partial fulfilment of the
requirements for the award of the degree of
Master of Engineering (Mechanical)
Faculty of Mechanical Engineering
Universiti Teknologi Malaysia
JANUARY 2015
iii
“To my beloved mother, father, my sister and my wife”
iv
ACKNOWLEDGEMENT
All praises are due to Allah the cherished, the Sustainer of the entire universe,
praise be to him, he who taught man with a pen, what he knew not. I asked Allah
Subhanahu wataallah to bestowed Peace and blessings upon His Messenger,
Muhammad S.A.W, and all his family and companions.
I like to express my profound gratitude to my supervisor, Dr. Raja Ishak bin
Raja Hamzah for his patience, advice, time sparing, useful comments, suggestion,
correction, concern and interest in my understanding of what a research undertaking
is, its development and write -up.
I would like to thank the staff of vibration Laboratory, and Faculty of
mechanical Engineering, Universiti Teknologi Malaysia for their understanding.
I will finally like to express my gratitude with a high degree of appreciation
to my friends for their love, care, concern and support both morally and financially,
my gratitude knows no bound. I say a big thank to them. All worlds most precious
resources would not adequately compensate for them love and sacrifices. With
Allah, however, is an adequate and lasting reward.
v
ABSTRACT
Monitoring defects in a pipeline are very important in particularly for buried
pipeline. The ability to identify location, type and size of the defects are always
necessary to save time and cost. Thus, this research project embarks on identifying
defect location and estimating the size of the defect by using Acoustic Emission (AE)
technology. Data were acquired from a two inch diameter pipe with 1mm thickness
using low cost piezoelectric sensors. Location of the defect was estimated based on
the difference on the time arrival coming from the two sensors. Results show that
location of the defect can be estimated within certain degree of accuracy. In addition,
different sizes of the defect have also been studied using artificially induced defect as
well as the actual defect. Defects have been introduced both in the axial and radial
direction on the test pipe. AE data from the pencil break has been collected for each
defect size and processed using few AE parameters such as R.M.S., energy,
amplitude and time-frequency domain. All of these data were then normalized to the
reference value and the correlation between defect sizes and normalized AE
parameters were developed. The results show that there are good correlations
between normalized AE parameter and defect sizes particularly when the defect size
was increased in axial direction. In short, it is shown that AE technology is capable
in locating defect in pipeline as well as giving estimation on the size of the defect.
vi
ABSTRAK
Pemantauan kerosakan ke atas talian paip adalah amat penting terutamanya
untuk talian paip yang tertanam. Kemampuan untuk mengenal pasti lokasi, jenis dan
saiz kerosakan sentiasa perlu untuk menjimatkan masa dan kos. Oleh itu, projek
penyelidikan ini dijalankan bagi mengenalpasti lokasi kerosakan dan menganggarkan
saiz kerosakan itu dengan menggunakan teknologi Pancaran Akustik (AE). Data
diperolehi daripada paip berdiameter dua inci berketebalan 1 mm menggunakan
sensor piezoelektrik kos rendah. Lokasi kerosakan tersebut dianggarkan berdasarkan
perbezaan pada masa ketibaan yang diberikan daripada kedua-dua sensor. Keputusan
menunjukkan bahawa lokasi kerosakan tersebut boleh dianggarkan pada tahap
ketepatan tertentu. Di samping itu, saiz kerosakan yang berbeza itu juga telah dikaji
dengan menggunakan aruhan kerosakan buatan serta kerosakan yang sebenar.
Kerosakan telah dikenakan ke atas kedua-dua arah paksi dan jejari pada paip ujian.
Data AE daripada patahan pensel telah dikumpulkan untuk setiap saiz kerosakan dan
diproses menggunakan beberapa parameter AE seperti R.M.S, tenaga, amplitud dan
domain masa-frekuensi. Semua data ini kemudiannya dinormalkan kepada nilai
rujukan dan korelasi antara saiz kerosakan dan parameter AE ternormal telah
dibangunkan. Hasil kajian menunjukkan bahawa terdapat korelasi yang baik antara
parameter AE ternormal dan saiz kerosakan terutamanya apabila saiz kerosakan itu
telah meningkat dalam arah paksi. Secara ringkasnya, ia menunjukkan bahawa
teknologi AE mampu mengenalpasti lokasi kerosakan ke atas talian paip serta
memberikan anggaran mengenai saiz kerosakan itu
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK Vi
TABLE OF CONTENTS Vii
LIST OF TABLES X
LIST OF FIGURES Xi
LIST OF APPENDICES xiii
1 INTRODUCTION 1
1.1 Introduction 1
1.2 Problem Statement 3
1.3 Objective of the Study 4
1.4 Scope of the Study 4
2 LITERATURE REVIEW 5
2.1 Introduction 5
2.1.1 Acoustic Emission Technique 5
2.2 AE Data Analysis Approaches 6
2.2.1 Parameter Based Analysis 6
2.2.2 Waveform Based Analysis 9
2.3 AE Wave Mode 10
viii
2.3.1 Longitudinal and Shear Waves 10
2.3.2 Surface Waves 11
2.3.3 Lamb Waves 12
2.4 Instrumentation for AE Monitoring 13
2.4.1 Data acquisition 14
2.4.2 Signal Processing Tools 16
2.5 Source Location Theory 17
2.6 Challenge in Using Acoustic Emission Technique 18
2.6.1 Source Localization 19
2.6.1.1 Time of Arrival Method 19
2.6.1.2 Source Location Using Modal
Analysis Technique 19
2.6.2 Noise Removal and Source Differentiation 21
2.7 Summary 21
3 METHODOLOGY 23
3.1 Introduction 23
3.2 Acoustic Emission 23
3.3 Research Design 24
3.4 Calculate Sensitivity of Sensor 25
3.5 Experimental Methods for Data Acquisition 26
3.5.1 Transducer 27
3.5.2 Specimen 28
3.6 Localize AE Source 29
3.7 Estimate Defect Size 32
4 RESULT AND DISCUSSION 34
4.1 Introduction 34
4.2 Source Location Results 35
4.3 The Results of Acoustic Emission Parameters 40
4.3.1 Results for Radial Defect 41
4.3.2 The Results of Longitudinal Defect 48
4.4 The Results of Real Defect (0.8 0.8) cm 53
ix
5 CONCLUSION AND RECOMMENDATIONS 57
5.1 Conclusion 57
5.2 Recommendations 58
REFERENCES 59
Appendices A-C 62-77
x
LIST OF TABLES
TABLE NO TITLE PAGE
3.1 Specification of buzzer 28
4.1 Results for time delay for all position's pencil lead
break 38
4.2 The results of measure and actual distance 39
4.3 Acoustic emission parameters for radial defect 45
4.4 Acoustic emission parameters for longitudinal
defect 50
4.5 Compare the results between artificial defect and
real defect 56
xi
LIST OF FIGURES
FIGURE NO TITLE PAGE
2.1 Acoustic emission technique 6
2.2 Parameters of AE signals 7
2.3 Energy as measure under rectified signal envelope 8
2.4 Continuous and burst AE signals 9
2.5 (a) Longitudinal and (b) Transverse waves
(continued) 11
2.6 Surface waves 12
2.7 Early arriving symmetric (extensional) mode and
later asymmetric (flexural) modes 12
2.8 Symmetric and asymmetric lamb waves 13
2.9 AE measurement chain 15
2.10 Different types of sensors 15
2.11 AE sensor of the piezoelectric element 16
3.1 Research of flowchart 25
3.2 sensitivity of sensor 26
3.3 Experimental setup 27
3.4 Piezoelectric buzzer 28
3.5 Pipe steel 29
3.6 Schematic pipe with two sensors 29
3.7 Pencil lead break 30
3.8 Pencil-lead break for each distance 31
3.9 Time arrival signals 31
3.10 Increase artificial defect in radial direction 32
xii
3.11 Increase artificial defect in axial direction 33
4.1 Time arrival signals at 10 cm hit from sensor 1 36
4.2 Time arrival signals at 20 cm hit from sensor 1 36
4.3 Time arrival signals at 30 cm hit from sensor 1 37
4.4 Time arrival signals at 40 cm hit from sensor 1 37
4.5 Comparison between actual and measure distance 39
4.6 (a) AE waveform (b) FFT for defect size (0.8 0.8)
cm recorded by sensor 1 41
4.7 (a) AE waveform and (b) FFT for defect size
(0.8 0.8) cm recorded by sensor 2 42
4.8 (a) AE waveform (b) FFT for defect size (0.8 1.6)
cm recorded by sensor 1 43
4.9 (a) AE waveform (b) FFT for defect size (0.8 1.6)
cm recorded by sensor 2 44
4.10 Normalization of amplitude for radial defect 46
4.11 Normalization of energy for radial defect 46
4.12 Normalization of RMS for radial defect 47
4.13 (a) AE waveform (b) FFT for defect size (2.4 6.4)
cm recorded by sensor 1 48
4.14 (a) AE waveform (b) FFT for defect size (2.4 6.4)
cm recorded by sensor 2 49
4.15 Normalization of amplitude for longitudinal defect 51
4.16 Normalization of RMS for longitudinal defect 51
4.17 Normalization of energy for longitudinal defect 52
4.18 Estimate the defect size 52
4.18 a) AE waveform (b) FFT for real defect size
(0.8 0.8) cm recorded by sensor 1 53
4.19 (a) AE waveform (b) FFT for real defect size
(0.8 0.8) cm recorded by sensor 2 54
4.20 Real Defect (0.8 x 0.8) cm 55
xiii
LIST OF ABBREVIATIONS
AE acoustic emission
SHM Structural Health Monitoring
NDT Non Destructive Testing
PB piezoelectric buzzer
PLB pencil lead break
TOA time of arrival
RMS Root Mean Square
dB decibel (unit of acoustic emission signal magnitude)
FFT Fast Fourier transforms
SNR signal noise ratio
WT wavelet transform
STFT short Fourier transform
xiv
LIST OF APPENDICES
APPENDIX TITLE PAGE
A MATLAB Code (pure tone ) 62
B Experimental Results Time Arrival Signals 63
C Data analysis for defect size 67
1
CHAPTER 1
INTRODUCTION
1.1 Introduction
Technology of pipeline has been developed in order to increase the potential and
ability of this type of transportation. Nowadays, pipelines have become a regular
practice to transfer the material from the raw source to the end user. Due to this
economic, transportation, pipelines become a major role in the petroleum industry
because this method is safe, reliable and can promise constant flow of crude oil, natural
gas and petroleum products.
Leak can occur from small holes caused by corrosion up to catastrophic pipeline
failure due to man-made damage or natural causes such as earthquakes. Because of this
matter, the development of pipe leak monitoring has been introduced and research will
keep running to improve the system from time to time. If the surface exposed, the
location of the leak can be determined by visual technique. But if the pipe is coated or
buried, the location of the leak or crack is difficult to be determined by visual technique.
Therefore, there are several non-destructive testing (NDT) techniques which can be
2
applied to this application to detect the earliest leaking condition. One of these
techniques called acoustic emission.
Acoustic Emission (AE) is a phenomenon of all materials where stress waves are
released due to micro-structural changes in the material. These micro-structural changes
could be propagating cracks or corrosion, but can also be caused by friction between two
parts of a structure. The term Acoustic Emission describes the stress waves as well as
the NDT technique which uses these stress waves. The waves travel through the
structure and result in small displacements on the surface, which can be detected by
suitable sensors [1]. AE is a passive NDT technique which means the instrument has
just a receiving unit which is capable of picking up the emission caused by the
propagation of the flaw itself. An active inspection method, in contrast, needs a
transmitting and receiving unit and measures the changes of a signal sent through the
structure.
The discovery of the generation of these stress waves by Joseph Kaiser in 1950
marks the beginning of Acoustic Emission as an NDT technique. Joseph Kaiser studied
this phenomenon occurring during tensile tests of metal specimens. Earlier, a similar
phenomenon was already widely studied prior or during earthquakes and this seismology
has much in common with AE. Since the first discovery AE has developed into a widely
used NDT technique.
Acoustic Emission has already been measurable from cracks down to a few
hundred square micrometers and is therefore able to detect flaws long before structural
integrity is affected and before most other NDT techniques, such as Ultrasonic, X-ray or
Thermography, can sense those cracks [2]. In the strict sense the flaw propagation is
measured, since only the active damage emits stress waves.
3
Acoustic Emission (AE) is a passive inspection technique which, unlike
Ultrasonic, does not actively send any signal through the structure, but detects stress
waves from the damage itself when it propagates. A few sensors can monitor a
relatively large area and can detect different types of damage usually long before other
NDT techniques are able to find them. Also, AE testing can be performed when the
structure is in service. This makes AE an ideal tool for SHM applications.
Accurate damage location is important for Acoustic Emission SHM (and NDT)
systems since this technique is able to detect signals from propagating damages in
relatively far distances from the sensor. Exact source location is not only important to
reduce time for further inspection and repairs but, also to identify multiple damage to
understand fatigue damage propagation or to find indications about damage origin.
Although it is already reported that the actual AE signal can give an indication about the
actual damage type, exact source location might also aid to fulfill this SHM task.
1.2 Problem Statement
Pipes are used widely in our domestic and industry. It is one of the most
important parts of human life because piping system is a mechanism of delivering or
transporting a fluid (liquid or gas), mixture of fluids and others from one location to
another. Pipes need to be monitored periodically in order to determine damage to the
pipe and its associated equipment, maximize the efficiency and safety of the pipeline,
minimize potential accidents, and safeguard company and public interests [1].
4
1.3 Objective of the Study
The main objective of this study is to localize defect in the pipeline using
acoustic emission and estimate the geometry of defect from acoustic emission signals.
1.4 Scope of the Study:
To use acoustic emission technology to monitor the pipeline.
To undertake experimental test in the lab scale rig.
To Analyze AE signals using MATLAB software.
59
REFERENCES
1. Wevers, M. 1997. Listening to the Sound Of Materials: Acoustic Emission for
the Analysis of Material Behaviour. NDT & E International, 30(2), 99-106.
2. Finlayson, R. D., Friesel, M., Carlos, M., Cole, P., and Lenain, J. C. 2001.
Health Monitoring of Aerospace Structures with Acoustic Emission and
Acousto Ultrasonics. Insight-Wigston Then Northampton-, 43 (3), 155-158.
3. Shull, P. J. 2002. Nondestructive Evaluation: Theory, Techniques, and
Applications. CRC Press.
4. Grosse, C. U. 2008. Acoustic Emission Testing (pp. 3-18). Heidelberg:
Springer.
5. Physical Acoustics Corporation. PCI-2 Based AE System Users Manual
Princeton Junction, NJ: Physical Acoustics Corporation; 2007.
6. Grosse, C. U., and Linzer, L. M. 2008. Signal-based AE Analysis. In Acoustic
Emission Testing (pp. 53-99). Springer Berlin Heidelberg.
7. Vallen, H. 2002. AE Testing Fundamentals, Equipment, Applications. Journal
of Nondestructive Testing (Germany), 7(9), 1-30.
8. Rose, J. L. 2004. Ultrasonic Waves in Solid Media. Cambridge university
press.
9. Finlayson, R. D., Luzio, M. A., Miller, R. K., and Pollock, A. A. 2003.
Continuous Health Monitoring of Graphite Epoxy Motorcases (GEM). In NDE
for Health Monitoring and Diagnostics (pp. 272-283). International Society for
Optics and Photonics.
10. Maji, A. K., Satpathi, D., and Kratochvil, T. 1997. Acoustic Emission Source
Location Using Lamb Wave Modes. Journal of Engineering Mechanics, 123(2),
154-161.
11. Holford, K. M., and Lark, R. J. 2005. Acoustic Emission Testing Bridges.
60
12. Grosse, C. U. 2008. Chapter 3 Sensor and Instrument. Acoustic Emission
Testing. Verlag: Springer
13. Yoon, D. J., Weiss, W. J., and Shah, S. P. 2000. Assessing Damage in
Corroded Reinforced Concrete Using Acoustic Emission. Journal of
Engineering Mechanics, 126(3), 273-283.
14. Qi, G. 2000. Wavelet-based AE Characterization of Composite Materials.
NDT and E International, 33(3), 133-144.
15. Grosse, C. U., Finck, F., Kurz, J. H., and Reinhardt, H. W. 2004.
Improvements of AE Technique Using Wavelet Algorithms, Coherence
Functions and Automatic Data Analysis. Construction and Building Materials,
18(3), 203-213
16. Grosse, C. U., Reinhardt, H. W., Motz, M., & Kroplin, B. 2002. Signal
Conditioning in Acoustic Emission Analysis Using Wavelets. NDT. net, 7(9),
1-9.
17. Ativitavas, N., Pothisiri,T., and T. Fowler, T. 2006. Identification of Fibre-
Reinforced Plastic Failure Mechanisms from Acoustic Emission Data Using
Neural Networks, 193-226.
18. Salinas, V., Vargas, Y., Ruzzante, J., and Gaete, L. 2010. Localization
Algorithm for Acoustic Emission. Physics Procedia, 3(1), 863-871
19. Ziola, S. M., and Gorman, M. R. 1991. Source Location in Thin Plates Using
Cross‐Correlation. The Journal of the Acoustical Society of America, 90(5),
2551-2556.
20. Scholey, J. J., Wilcox, P. D., Wisnom, M. R., & Friswell, M. I. 2009. A
Practical Technique for Quantifying the Performance of Acoustic Emission
Systems on Plate-Like Structures. Ultrasonics, 49(6), 538-548
21. Chang, P. C., Flatau, A., and Liu, S. C. 2003. Review Paper: Health
Monitoring of Civil Infrastructure. Structural Health Monitoring, 2(3), 257-
267.
22. Holford, K. M., Davies, A. W., Pullin, R., and Carter, D. C. 2001. Damage
Location in Steel Bridges by Acoustic Emission. Journal of Intelligent
Material Systems and Structures, 12(8), 567-576
23. Lozev, M. G. 1997. Acoustic Emission Monitoring of Steel Bridge Members
(No. FHWA/VTRC 97-R13).
61
24. Nivesrangsan, P., Steel, J. A., & Reuben, R. L. 2007. Source Location of
Acoustic Emission in Diesel Engines. Mechanical Systems and Signal
Processing, 21(2), 1103-1114.
25. Tobias, A. 1976. Acoustic-Emission Source Location in Two Dimensions by
an Array of Three Sensors. Non-Destructive Testing, 9(1), 9-12.
26. Shigeishi, M., Colombo, S., Broughton, K. J., Rutledge, H., Batchelor, A. J., &
Forde, M. C. 2001. Acoustic Emission to Assess and Monitor the Integrity of
bridges. Construction and Building Materials, 15(1), 35-49.
27. Wevers, M., & Lambrighs, K. 2009. Applications of Acoustic Emission for
SHM: A review. Encyclopedia of Structural Health Monitoring.
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