Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
Abstract
:1. Introduction
2. Experimental Setup and Data Acquisition
3. The Proposed Methodology of ECFAR for Burst Detection and Pipeline Fault Diagnosis
3.1. Burst Detection Use Enhance Constant Fault Alarm Rate
3.2. Leakage Detection for Pipeline with One-Versus-All Multiclass Support Vector Machine
4. Pipeline Fault Experiment Results Analysis
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Murvay, P.-S.; Silea, I. A survey on gas leak detection and localization techniques. J. Loss Prev. Process. Ind. 2012, 25, 966–973. [Google Scholar] [CrossRef]
- Datta, S.; Sarkar, S. A review on different pipeline fault detection methods. J. Loss Prev. Process. Ind. 2016, 41, 97–106. [Google Scholar] [CrossRef]
- Chan, T.K.; Chin, C.S.; Zhong, X. Review of Current Technologies and Proposed Intelligent Methodologies for Water Distributed Network Leakage Detection. IEEE Access 2018, 6, 78846–78867. [Google Scholar] [CrossRef]
- Liu, Z.; Kleiner, Y. State of the art review of inspection technologies for condition assessment of water pipes. Measurement 2013, 46, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Adedeji, K.B.; Hamam, Y.; Abe, B.T.; Abu-Mahfouz, A.M. Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: An overview. IEEE Access 2017, 5, 20272–20285. [Google Scholar] [CrossRef]
- Bui Quy, T.; Muhammad, S.; Kim, J.-M. A reliable acoustic EMISSION based technique for the detection of a small leak in a pipeline system. Energies 2019, 12, 1472. [Google Scholar]
- Martini, A.; Troncossi, M.; Rivola, A. Leak detection in water-filled small-diameter polyethylene pipes by means of acoustic emission measurements. Appl. Sci. 2017, 7, 2. [Google Scholar] [CrossRef] [Green Version]
- Xiao, Q.; Li, J.; Sun, J.; Feng, H.; Jin, S. Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time–frequency spectrum. Measurement 2018, 124, 163–172. [Google Scholar] [CrossRef]
- Zhu, S.-B.; Li, Z.-L.; Zhang, S.-M.; Liang, L.-L.; Zhang, H.-F. Natural gas pipeline valve leakage rate estimation via factor and cluster analysis of acoustic emissions. Measurement 2018, 125, 48–55. [Google Scholar] [CrossRef]
- Xiao, R.; Hu, Q.; Li, J. Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine. Measurement 2019, 146, 479–489. [Google Scholar] [CrossRef]
- Song, Y.; Li, S. Leak detection for galvanized steel pipes due to loosening of screw thread connections based on acoustic emission and neural networks. J. Vib. Control. 2018, 24, 4122–4129. [Google Scholar] [CrossRef]
- Hernandez Crespo, B.; Courtney, C.R.P.; Engineer, B. Calculation of Guided Wave Dispersion Characteristics Using a Three-Transducer Measurement System. Appl. Sci. 2018, 8, 1253. [Google Scholar] [CrossRef] [Green Version]
- He, P. Simulation of ultrasound pulse propagation in lossy media obeying a frequency power law. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 1998, 45, 114–125. [Google Scholar] [PubMed] [Green Version]
- Mostafapour, A.; Davoodi, S. A theoretical and experimental study on acoustic signals caused by leakage in buried gas-filled pipe. Appl. Acoust. 2015, 87, 1–8. [Google Scholar] [CrossRef]
- Thomas, M.J.; Jay, N.M.; Daniel, J.W. Acoustic emission leak detection on a metal pipeline buried in sandy soil. J. Pipeline Syst. Eng. Pract. 2013, 4, 149–155. [Google Scholar]
- Khulief, Y.A.; Khalifa, A.E.; Ben-Mansour, R.; Habib, M.A. Acoustic detection of leaks in water pipelines using measurements inside pipe. J. Pipeline Syst. Eng. Pract. 2012, 3, 47–54. [Google Scholar] [CrossRef]
- Antaki, G.A. Piping and Pipeline Engineering: Design, Construction, Maintenance, Integrity, and Repair; CRC Press: Boca Raton, FL, USA, 2003; ISBN 978-0-429-21345-8. [Google Scholar]
- Gao, Y.; Brennan, M.J.; Joseph, P.F.; Muggleton, J.M.; Hunaidi, O. A model of the correlation function of leak noise in buried plastic pipes. J. Sound Vib. 2004, 277, 133–148. [Google Scholar] [CrossRef]
- Nicola, M.; Nicola, C.-I.; Vintilă, A.; Hurezeanu, I.; Duță, M. Pipeline Leakage Detection by Means of Acoustic Emission Technique Using Cross-Correlation Function. J. Mech. Eng. Autom. 2018, 8, 59–67. [Google Scholar]
- Quy, T.B.; Kim, J.-M. Leak localization in industrial-fluid pipelines based on acoustic emission burst monitoring. Measurement 2020, 151, 107150. [Google Scholar] [CrossRef]
- Srirangarajan, S.; Allen, M.; Preis, A.; Iqbal, M.; Lim, H.B.; Whittle, A.J. Wavelet-based burst event detection and localization in water distribution systems. J. Signal Process. Syst. 2013, 72, 1–16. [Google Scholar] [CrossRef] [Green Version]
- Pan, S.; Xu, Z.; Li, D.; Lu, D. Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology. Sensors 2018, 18, 3628. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, C.; Wen, Y.; Li, P.; Wen, J. Adaptive noise cancellation based on EMD in water-supply pipeline leak detection. Measurement 2016, 79, 188–197. [Google Scholar] [CrossRef]
- Acosta, G.G.; Villar, S.A. Accumulated CA–CFAR process in 2-D for online object detection from sidescan sonar data. IEEE J. Ocean. Eng. 2015, 40, 558–569. [Google Scholar] [CrossRef]
- Physicalacoustics-Pci 2. Available online: https://www.physicalacoustics.com/by-product/pci-2/ (accessed on 3 March 2020).
- Physicalacoustics-Sensors. Available online: https://www.physicalacoustics.com/by-product/sensors/WDI-AST-100-900-kHz-Wideband-Differential-AE-Sensor (accessed on 3 March 2020).
- Jarabo-Amores, M.-P.; de la Mata-Moya, D.; Gil-Pita, R.; Rosa-Zurera, M. Radar detection with the Neyman–Pearson criterion using supervised-learning-machines trained with the cross-entropy error. EURASIP J. Adv. Signal Process. 2013, 2013, 44. [Google Scholar] [CrossRef] [Green Version]
- Manjurul Islam, M.M.; Kim, J.-M. Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines. Reliab. Eng. Syst. Saf. 2019, 184, 55–66. [Google Scholar] [CrossRef]
- Kang, M.; Kim, J.; Wills, L.M.; Kim, J.-M. Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis. IEEE Trans. Ind. Electron. 2015, 62, 7749–7761. [Google Scholar] [CrossRef]
Devices | Detail Characteristics | |
---|---|---|
WDI-AST |
| |
PCI 2 |
| |
Scenario (1) | Scenario (2) | Scenario (3) | |
---|---|---|---|
k-NN | 70.81% | 85.65% | 86.76% |
MLP | 69.70% | 78.98% | 83.40% |
OAA-SVMs | 73.64% | 87.00% | 93.00% |
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Duong, B.P.; Kim, J.; Jeong, I.; Kim, C.H.; Kim, J.-M. Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline. Appl. Sci. 2020, 10, 1933. https://doi.org/10.3390/app10061933
Duong BP, Kim J, Jeong I, Kim CH, Kim J-M. Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline. Applied Sciences. 2020; 10(6):1933. https://doi.org/10.3390/app10061933
Chicago/Turabian StyleDuong, Bach Phi, JaeYoung Kim, Inkyu Jeong, Cheol Hong Kim, and Jong-Myon Kim. 2020. "Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline" Applied Sciences 10, no. 6: 1933. https://doi.org/10.3390/app10061933
APA StyleDuong, B. P., Kim, J., Jeong, I., Kim, C. H., & Kim, J.-M. (2020). Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline. Applied Sciences, 10(6), 1933. https://doi.org/10.3390/app10061933