Classification of Hydrate Blockage and Pipeline Leakage in Natural Gas Pipelines Based on EMD and SVM
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 13, Issue 1
Abstract
In this paper, pipeline abnormal events (hydrate blockage and pipeline leakage) were detected by an active acoustic excitation method based on pulse compression. The positions of pipeline abnormal events were determined by time delay between emitted and reflected signals. Besides, in order to effectively distinguish the two detection signals, the empirical mode decomposition (EMD) method was used to obtain the intrinsic mode function (IMF) of the detection signal, and the normalized energy of all IMF components was used as eigenvector to input to the support vector machine (SVM) classifier for classification. The experiment results demonstrated that the trained models could accurately classify hydrate blockage and pipeline leakage after adjusting the classification threshold by the Youden index. The evaluation indicators including accuracy, precision, recall, specificity, and F1-score were 100% and area under curve (AUC) was 1 in testing set.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was funded by the National Natural Science Foundation of China (No. 61901299).
References
An, Y., X. C. Wang, B. Yue, S. Jin, L. Q. Wu, and Z. G. Qu. 2019. “A novel method for natural gas pipeline safety online monitoring based on acoustic pulse compression.” J. Process Saf. Environ. Prot. 130 (Oct): 174–181. https://doi.org/10.1016/j.psep.2019.08.008.
An, Y., X. C. Wang, B. Yue, L. Q. Wu, Z. G. Qu, Y. Liu, H. H. Yue, and D. X. Yan. 2018. “Natural gas pipeline safety monitoring technique based on chaotic characteristics of the detected signals.” J. Process Saf. Environ. Prot. 118 (Aug): 279–284. https://doi.org/10.1016/j.psep.2018.07.007.
ASTM. 2017. Standard specification for electric-resistance-welded coiled steel tubing for gas and fuel oil lines. ASTM A539-90. West Conshohocken, PA: ASTM.
Bradley, P. 1997. “The use of the area under the ROC curve in the evaluation of machine learning algorithms.” Pattern Recogn. 30 (7): 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2.
Chen, L. S., E. B. Zhao, D. H. Wang, Z. G. Han, S. W. Zhang, and C. P. Xu. 2010. “Feature extraction of EEG signals from epilepsy patients based on Gabor transform and EMD decomposition.” In Proc., 6th Int. Conf. on Natural Computation, 1243–1247. New York: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICNC.2010.5583630.
Cortes, C., and V. Vapnik. 1995. “Support-vector networks.” J. Mach. Learn. 20 (3): 273–295. https://doi.org/10.1023/A:1022627411411.
Cox, S. J., D. J. F. Taylor, T. G. A. Youngs, A. K. Soper, T. S. Totton, R. G. Chapman, M. Arjmandi, M. G. Hodges, N. T. Skipper, and A. Michaelides. 2018. “Formation of methane hydrate in the presence of natural and synthetic nanoparticles.” J. Am. Chem. Soc. 140 (9): 3277–3284. https://doi.org/10.1021/jacs.7b12050.
Duan, W. B., R. Kirby, J. Prisutova, and K. V. Horoshenkov. 2015. “On the use of power reflection ratio and phase change to determine the geometry of a blockage in a pipe.” J. Appl. Acoust. 87 (Jan): 190–197. https://doi.org/10.1016/j.apacoust.2014.07.002.
Huang, N. E., S. Zheng, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. Yen, C. C. Tung, and H. H. Liu. 1998. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” J. Proc. Math. Phys. Eng. Sci. 454 (1971): 903–995. https://doi.org/10.1098/rspa.1998.0193.
Li, Z. L., H. F. Zhang, D. J. Tan, X. Chen, and H. X. Lei. 2017. “A novel acoustic emission detection module for leakage recognition in a gas pipeline valve.” J. Process Saf. Environ. Prot. 105 (Jan): 32–40. https://doi.org/10.1016/j.psep.2016.10.005.
Lile, N. L. T., M. J. Hasnul, R. A. Siregar, and J. C. Leong. 2013. “Effect of blockage size on pipe vibration.” J. Adv. Mater. Res. 626: 993–996. https://doi.org/10.4028/www.scientific.net/AMR.626.993.
Platt, J. C. 1999. “Fast training of support vector machines using sequential minimal optimization, in advances in Kernel Methods.” In Support vector learning, 185–208. Cambridge, MA: MIT Press.
Pontil, M., and A. Verri. 1998. “Support vector machines for 3D object recognition.” J. IEEE Trans. Pattern Anal. Mach. Intell. 20 (6): 637–646. https://doi.org/10.1109/34.683777.
Qu, Z. G., et al. 2017. “Study on the natural gas pipeline safety monitoring technique and the time-frequency signal analysis method.” J. Loss Prev. Proc. 47 (May): 1–9. https://doi.org/10.1016/j.jlp.2017.02.016.
Qu, Z. G., H. Y. Wang, Y. An, H. H. Yue, J. Li, Y. Zhang, Y. F. Wang, T. T. Yue, and W. B. Zhou. 2016. “Online monitoring method of hydrate agglomeration in natural gas pipelines based on acoustic active excitation.” J. Meas. 92 (Oct): 11–18. https://doi.org/10.1016/j.measurement.2016.05.084.
Sloan, E. D. 2008. Clathrate hydrates of natural gases. 3rd ed. Boca Raton, FL: CRC Press.
Sun, J. D., Q. Y. Xiao, J. T. Wen, and F. Wang. 2014. “Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM.” J. Meas. 55 (Sep): 434–443. https://doi.org/10.1016/j.measurement.2014.05.012.
Youden, W. J. 1950. “Index for rating diagnostic tests.” Cancer 3 (1): 32–35. https://doi.org/10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 13 • Issue 1 • February 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Dec 14, 2020
Accepted: Oct 8, 2021
Published online: Nov 25, 2021
Published in print: Feb 1, 2022
Discussion open until: Apr 25, 2022
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