Issues concerning natural gas pipeline leakage are becoming more prominent than ever because of the continuing expansion of natural gas pipeline networks. Although many scholars have extensively investigated generation and detection methods for pipeline leakage acoustic signals, systematic research on the characteristics of leakage and interference signals remains insufficient. Results show that the method based on the RBF kernel function is feasible for pipeline fault diagnosis, yielding 100% sensitivity, 92% specificity, and 96% accuracy.
AhadiMBakhtiarMS (2010) Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to acoustic emission signals. Applied Acoustics71(7): 634–639.
2.
AnXLZengHT (2015) Pressure fluctuation signal analysis of a hydraulic turbine based on variational mode decomposition. Power and Energy229(8): 978–991.
3.
BenkheroufAAllidinaAY (1988) Leak detection and location in gas pipelines. Control Theory and Applications, IEEE Proceedings D [see also IEE Proceedings-Control Theory and Applications], 135(2): 142–148.
DaubechiesI (1990) The wavelet transform, time-frequency localization and signal analysis. Journal of Renewable & Sustainable Energy36(5): 961–1005.
6.
De-YiLILiuCYYiDU, et al. (2004) Artificial intelligence with uncertainty. Journal of Software15(11): 1583–1593.
7.
DragomiretskiyKZossoD (2014) Variational mode decomposition. IEEE Transactions on Signal Processing62(3): 531–544.
8.
DuWLiuWXuZ (2010) A cloud model theory based trust inference mechanism. In: 2010 Fifth Annual ChinaGrid Conference, pp. 258–261. IEEE.
9.
FerranteMBrunoneB (2003) Pipe system diagnosis and leak detection by unsteady-state tests. 2. wavelet analysis. Advances in Water Resources26(1): 107–116.
10.
FuQWanHJQiuF (2010) Pipeline leak detection based on fiber optic early-warning system. Procedia Engineering7: 88–93.
11.
FuWZhouJZhangY (2015) Fault diagnosis for rolling element bearings with VMD time-frequency analysis and SVM. In: 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), pp. 69–72. IEEE.
12.
GuptaKKRajuKS (2014) Bearing fault analysis using variational mode decomposition. In: 2014 9th International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. IEEE.
13.
HanMPanJ (2015) A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings. Measurement76: 7–19.
14.
HeYZhangX (2012) Approximate entropy analysis of the acoustic emission from defects in rolling element bearings. Journal of Vibration & Acoustics134(6): 969–970.
15.
HuangNEShenZLongSR, et al. (1998). The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings A454(1971): 903–995.
16.
KimMSLeeSK (2009) Detection of leak acoustic signal in buried gas pipe based on the time–frequency analysis. Journal of Loss Prevention in the Process Industries22(6): 990–994.
17.
LahmiriSBoukadoumM (2014) Biomedical image denoising using variational mode decomposition. In: 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings, pp. 340–343. IEEE.
18.
LangXLiPHuZ, et al. (2017) Leak detection and location of pipelines based on LMD and least squares twin support vector machine. IEEE Access5: 8659–8668.
19.
LiCJZhangJPZhangZQ, et al. (2014) The research on leak detection technology of natural gas pipeline based on EMD. In: Applied Mechanics and Materials, Vol. 494, pp. 793–796. Trans Tech Publications.
20.
LiYLiJSunL, et al. (2009) Pipeline leak detection based on acoustic emission using empirical mode decomposition method. In: 2008 7th International Pipeline Conference, pp. 493–497. American Society of Mechanical Engineers Digital Collection.
21.
LiZZhangHTanD, et al. (2017) A novel acoustic emission detection module for leakage recognition in a gas pipeline valve. Process Safety and Environmental Protection105: 32–40.
22.
LiuCWLiYXWangWC, et al. (2013) Theoretical and experimental study on leak detection for natural gas pipelines based on acoustic method. Chinese Journal of Acoustics38(3): 372–381.
23.
MengLLiYSongL, et al. (2010) Acoustic propagation characteristics, position monitoring and locating of gas transmission pipeline leakage. Natural Gas Industry30(11): 74–79.
24.
MengLLiYWangW, et al. (2012) Experimental study on leak detection and location for gas pipeline based on acoustic method. Journal of Loss Prevention in the Process Industries25(1): 90–102.
25.
MohantySGuptaKKRajuKS (2014) Comparative study between VMD and EMD in bearing fault diagnosis. In: 2014 9th International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. IEEE.
26.
MpeshaWHanif ChaudhryMGassmanSL (2002) Leak detection in pipes by frequency response method using a step excitation. Journal of Hydraulic Research40(1): 55–62.
27.
QuZYanZZengZ, et al. (2008) Detection of the abnormal events along the oil and gas pipeline and multi-scale chaotic character analysis of the detected signals. Measurement Science & Technology19(2): 025301.
28.
RajSRayKC (2017) Application of variational mode decomposition and ABC optimized DAG-SVM in arrhythmia analysis. In: 2017 7th International Symposium on Embedded Computing and System Design (ISED), pp. 1–5. IEEE.
29.
SunGQChenTWeiZHN, et al. (2016) A carbon price forecasting model based on variational mode decomposition and spiking neural networks. Energy9(1): 1–16.
30.
SunJWenJ (2013) Target location method for pipeline pre-warning system based on HHT and time difference of arrival. Measurement46(8): 2716–2725.
31.
TangBDongSTaoS (2012) Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation. Signal Processing92(1): 248–258.
32.
VapnikVN (1998) Statistical Learning Theory. New York: Wiley.
33.
WangY (2015) The application of vibration detection technology in pipeline leakage based on laser self-mixing interference. PhD Thesis, Northeast Petroleum University, China.
34.
WeiCQingxuanJHanxuS (2013) Bearing fault classification using support vector machine decision tree and grey correlation information entropy. Journal of Vibration, Measurement & Diagnosis33(3): 504–508.
35.
XiaoQLiJSunJ, et al (2018) Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time–frequency spectrum. Measurement124: 163–172.
36.
YangZXiongZShaoM (2010) A new method of leak location for the natural gas pipeline based on wavelet analysis. Energy35(9): 3814–3820.
37.
ZadkaramiMShahbazianMSalahshoorK (2016) Pipeline leakage detection and isolation: an integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN). Journal of Loss Prevention in the Process Industries43: 479–487.
38.
ZhangXJ (1993) Statistical leak detection in gas and liquid pipelines. Pipes & Pipelines International38(4): 26–29.
39.
ZhengWWangZHuangH, et al. (2015) A failure prediction approach based on cloud theory and hidden Markov model in networked computing systems. In: 2015 IEEE Symposium on Computers and Communication (ISCC), pp. 520–525. IEEE.
