Multi-point leakage locating method based on TFA and improved DE

LANG Xianming1,2,3, ZHU Yongqiang1, LI Jinna1, CAO Jiangtao1, LI Ping1, SONG Huadong3

Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (1) : 39-45.

PDF(2087 KB)
PDF(2087 KB)
Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (1) : 39-45.

Multi-point leakage locating method based on TFA and improved DE

  • LANG Xianming1,2,3, ZHU Yongqiang1, LI Jinna1, CAO Jiangtao1, LI Ping1, SONG Huadong3
Author information +
History +

Abstract

When multiple leaks occur at the same time, the influence of leak acoustic wave signals is superposed in the leak acoustic wave signal, which affects the propagation and attenuation rule of the transient acoustic signal. The traditional leak localization method is impossible to effectively locate the multiple leak signals under the condition of mixed signal. Thus, a localization method of multiple leaks based on time-frequency analysis of acoustic wave and improved differential evolution is proposed. Since the decomposition model number of Variational Mode Decomposing (VMD) affects the feature extraction, the energy function is used to improve VMD. The improved VMD is applied to acoustic wave analysis and processing. Additionally, the number of multiple leaks is extracted by Time-frequency Analysis (TFA). Then, the multiple leaks localization function is obtained. Differential Evolution (DE) is easy to fall into local optimum, and its convergence speed is slow in the later stage of evolution, Particle Swarm Optimization Algorithm (PSO) is used to improve the global convergence speed of DE, and the multiple leaks positions are calculated by improved differential evolution. By analyzing the acquired acoustic wave data of multiple leaks in experimental pipeline, the results show that the proposed method can accurately locate multiple leaks in pipeline, and the minimum error of leak localization is 18 m.

Key words

 multiple leaks;variational mode decomposing / time-frequency analysis / differential evolution

Cite this article

Download Citations
LANG Xianming1,2,3, ZHU Yongqiang1, LI Jinna1, CAO Jiangtao1, LI Ping1, SONG Huadong3. Multi-point leakage locating method based on TFA and improved DE[J]. Journal of Vibration and Shock, 2022, 41(1): 39-45

References

[1] 金浩,张来斌,梁伟,叶迎春,丁其坤.天然气管道泄漏声源特性及传播机理数值模拟[J].石油学报, 2014, 35(1):172-177.
Jin Hao, Zhang Laibin, Liang Wei, Ding Qikun. Simulation research on leak source characteristics and propagation mechanism for natural gas pipeline[J]. Acta Petrolei Sinica, 2014, 35(1):172-177.
[2] X. Wang and M. S. Ghidaoui. Identification of multiple leaks in pipeline II: Iterative beamforming and leak number estimation[J]. Mechanical Systems and Signal Processing, 2019, 119, 346-362.
[3] 刘翠伟,敬华飞,方丽萍,徐明海.输气管道泄漏声波衰减模型的理论研究[J].振动与冲击, 2018, 20:109-114.
Liu Cuiwei, Jing Huafei, Fang Lipeng, Xu Minghai. A theoretical study on the attenuation model of leakage acoustic waves for natural gas pipelines[J]. Journal of Vibration and Shock, 2018, 20:109-114.
[4] X. Wang and M. S. Ghidaoui, Identification of multiple leaks in pipeline: Linearized model, maximum likelihood, and super-resolution localization[J]. Mechanical Systems and Signal Processing, 2018, 107: 529-548.
[5] C. Liu, Z. Cui, L. Fang, Y. Li and M. Xu. Leak localization approaches for gas pipelines using time and velocity differences of acoustic waves[J]. Engineering Failure Analysis, 2019, 103:1-8.
[6] X. Lang, P. Li, J. Cao, Y. Li, and H. Ren,. A Small Leak Localization Method for Oil Pipelines Based on Information Fusion[J]. IEEE Sensors Journal, 2018, 18: 6115-6122.
[7] J. Liu, D. Zang, C. Liu, Y. Ma, and M. Fu. A leak detection method for oil pipeline based on markov feature and two-stage decision scheme[J]. Measurement, 2019, 138: 433-445 .
[8] C. Guo, Y. Wen, P. Li, and  J. Wen. Adaptive noise cancellation based on EMD in water-supply pipeline leak detection[J]. Measurement, 2016, 79:188-197.
[9] H. Ren, W. Liu, M. Shane, and  X. Wang. A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning[J]. Measurement, 2019, 148:1-8.
[10] J. Li, Y. Chen, Z. Qian, and C. Lu. Research on VMD based adaptive denoising method applied to water supply pipeline leakage location[J].  Measurement, 2020, 151:1-13.
[11] C. Liu, Y. Li, L. Fang, and M. Xu. New leak-localization approaches for gas pipelines using acoustic waves[J]. Measurement, 2019, 134:54-65.
[12] Q. Chen, G. Shen, J. Jiang, X. Diao, Z. Wang, L. Ni, and Z. Dou. Effect of rubber washers on leak location for assembled pressurized liquid pipeline based on negative pressure wave method[J]. Process Safety and Environmental Protection, 2018, 119:181-190.
[13] 郑晓亮,王强,薛生,袁宏永,付明.基于延迟求和的输气管道泄漏声波定位方法[J].仪器仪表学报, 2019, 11:241-249.
Zheng Xiaoliang, Wang Qiang, Xue Sheng, Yuan Hongyong, Fu Ming. Leakage localization for gas pipelines based on delay-and-sum using acoustic signal[J]. Chinese Journal of Scientific Instrument, 2019, 11:241-249.
[14] 王芳,林伟国,常新禹,邱宪波.基于信号增强的缓慢泄漏检测方法[J].化工学报, 2019, 12:4894-4906.
Wang Fang, Lin Weiguo, Chang Xinyu, Qiu Xianbo. Slow leak detection method based on signal enhancement[J]. CIESC Journal, 2019, 12:4894-4906.
[15] X. Lang, P. Li, Y. Guo, J. Cao, and S. Lu. A multiple leaks localization method in a pipeline based on change in the sound velocity[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(7):5010-5017.
[16] A. Goudarzi and M. Haeri, Multi-leak localization in liquid pipelines[C]. 2019 27th Iranian Conference on Electrical Engineering (ICEE), Yazd, Iran, 2019, 929-932.
[17] X. Lang, P. Li, Y. Guo, J. Cao and S. Lu, A multiple leaks’ localization method in a pipeline based on change in the sound velocity[J]. IEEE Transactions on Instrumentation and Measurement, 69(7):5010-5017, 2020.
[18] W.Liu, S. Cao,  Y. Chen. Seismic time–frequency analysis via empirical wavelet transform," IEEE Geoscience and Remote Sensing Letters[J].  2016, 13: 28-32.
[19] 丁传俊, 张相炎,刘宁. 基于改进反向差分进化算法的多股簧响应模型参数辨识[J].振动与冲击, 2019, 1:187-194.
Ding Chuanjun, Zhang Xiangyan, Liu Ning. Parametric identification for nonlinear response model of a stranded wire helical spring based on improved reverse learning difference evolution Algorithm[J]. Journal of Vibration and Shock, 2019, 1:187-194.
[20] N. Naimul, I. Hitoshi. Accelerating differential evolution using an adaptive local search[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1):101-125.
[21] W. Gong, Z Cai, L Jiang. Enhancing the performance of differential evolution using orthogonal design method[J]. Applied Mathematics and Computation, 2008, 206(1): 55-66.
[22] J. Schmitt, E. Horne, N. Pustelnik, S. Joubaud and P. Odier. An improved variational mode decomposition method for internal waves separation[C]. 2015 23rd European Signal Processing Conference (EUSIPCO), Nice, 1935-1939, 2015.
[23] J. Cheng, D. Yu, and Y. Yang. Application of support vector regression machines to the processing of end effects of Hilbert–Huang transform[J]. Mechanical Systems and Signal Processing, 21, 3:1197–1211, 2007.
[24] J. Li, C. Wang, Q. Zheng and Z. Qian. Leakage localization for long distance pipeline based on compressive sensing[J]. IEEE Sensors Journal, 19(16): 6795-6801, 2019.
[25] F. Pinto and M. Vetterli. Space-time-frequency processing of acoustic wave fields: theory, algorithms, and applications[J]. IEEE Transactions on Signal Processing, 58(9):4608-4620, 2010.
[26] K. Kadowaki, S. Nishimoto and I. Kitani. Measurement of pressure wave from AC tree in polymeric insulators and time-frequency analysis using wavelet transform[C]. Proceedings of the 2004 IEEE International Conference on Solid Dielectrics, 2004. ICSD 2004., Toulouse, France,  2:723-726, 2004.
[27] 尹爱军, 李海珠, 李江, 戴宗贤. Wigner-Ville分布复小波相似性评价及应用[J].振动、测试与振动, 2020, 1:7-11.
Yin Aijun, Li Haizhu, Li Jiang, Dai Zongxian. Complex Wavelet Structural Similarity Evaluation of Wigner-Ville Distribution and Bearing Early Condition Assessment[J]. Journal of Vibration,Measurement & Diagnosis, 2020, 1:7-11.
[28] X. Li, H. Zhang and Z. Lu. A differential evolution algorithm based on multi-population for economic dispatch problems with valve-point effects[J]. IEEE Access, 7: 95585-95609, 2019.
[29] K. Mistry, L. Zhang, S. C. Neoh, C. P. Lim and B. Fielding. A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition[J]. IEEE Transactions on Cybernetics, 47,(6):1496-1509,2017.
PDF(2087 KB)

Accesses

Citation

Detail

Sections
Recommended

/