Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

LIU Junqi; WEN Tao; XIE Guo; CAO Yuan; Roberts Clive

doi:10.23919/cje.2022.00.234

Volume 33 Issue 3

May 2024

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Article Navigation > Chinese Journal of Electronics > 2024 > 33(3): 814-822

Junqi LIU, Tao WEN, Guo XIE, et al., “Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 814–822, 2024 doi: 10.23919/cje.2022.00.234

Citation:

Junqi LIU, Tao WEN, Guo XIE, et al., “Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 814–822, 2024 doi: 10.23919/cje.2022.00.234

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

doi: 10.23919/cje.2022.00.234

LIU Junqi^1
,,
WEN Tao^2
,,
XIE Guo^1
,,
CAO Yuan^{2
,
,},
Roberts Clive^3
,

1.
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China
2.
The School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
3.
Birmingham Centre for Railway Research and Education, University of Birmingham, Birmingham, B15 2TT, UK

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Author Bio:
Junqi LIU received the B.S. and M.S. degrees in electronic information engineering from Donghua University, Shanghai, China. He is currently pursuing the Ph.D. degree in control science and engineering with Xi’an University of Technology, Xi’an, China. His research interests include intelligent information processing and fault diagnosis in the railway signaling systems. (Email: 1210313022@stu.xaut.edu.cn)

Tao WEN received the Ph.D. degree from the Birmingham Centre for Railway Research and Education at the University of Birmingham, Birmingham, UK, in 2018. He is now a Professor with the Beijing Jiaotong University. His research interests include federated learning, Internet of things, train control system optimization, railway conditional monitoring, machine learning, and digital filter research. (Email: wentao@bjtu.edu.cn)

Guo XIE received the B.S. and M.S. degrees from Xi’an University of Technology, China, in 2005 and 2008, respectively, and the D.E. degree from Nihon University, Tokyo, Japan, in 2013. He received Monbukagakusho, a Japanese Government Scholarship, from the Japanese Ministry of Education, Culture, Sports, Science and Technology. He is currently a Professor with Xi’an University of Technology. His research interests include safety and reliability of railway systems, optimal control, and stochastic control. He is a member of CAA and CCF. (Email: guoxie@xaut.edu.cn)

Yuan CAO received the Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University, Beijing, China, in 2011. He is currently a Professor with the National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University. His research interest includes health management in high-speed railway systems. (Email: ycao@bjtu.edu.cn)

Clive Roberts is Professor of Railway Systems at the University of Birmingham. He is Director of the Birmingham Centre for Railway Research and Education, which is the largest railway research group in Europe with just over 100 researchers. He works extensively with the railway industry and academia in Britain and overseas. He leads a broad portfolio of research aimed at improving the performance of railway systems, including a leading a strategic partnership in the area of data integration with Network Rail. His research interests include railway traffic management, condition monitoring, energy simulation and system integration. (Email: c.roberts.20@bham.ac.uk)
Corresponding author: Email: ycao@bjtu.edu.cn
Received Date: 2022-07-25
Accepted Date: 2023-01-16

Available Online: 2023-06-30

Publish Date: 2024-05-05

Abstract

Abstract

The fault diagnosis of railway point machines (RPMs) has attracted the attention of engineers and researchers. Seldom have studies considered diverse noises along the track. To fulfill this aspect, a multi-time-scale variational mode decomposition (MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises. MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees. In this manner, the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale. In the experimental validation, a random position, random type, random number, and random length (4R) noise-adding algorithm helps to verify the robustness of the approach. The adequate experimental results demonstrate the superiority of the proposed MTSVMD-based fault diagnosis.
- Railway point machines,
- Fault diagnosis,
- Multi-time-scale,
- Variational mode decomposition

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References(27)

References

[1]	National Railway Administration of the People’s Republic of China, “Railway statistics bulletin 2022,” Available at: https://www.nra.gov.cn/xwzx/xwxx/xwlb/202305/t20230529_341754.shtml, 2023-05-29. (in Chinese)
[2]	T. Wen, G. Xie, Y. Cao, et al., “A DNN-based channel model for network planning in train control systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2392–2399, 2022. doi: 10.1109/TITS.2021.3093025
[3]	D. C. Zhang, M. Xie, J. Y. Yang, et al., “Multi-sensor graph transfer network for health assessment of high-speed rail suspension systems,” 10.1109/TITS.2023.3265401">IEEE Transactions on Intelligent Transportation Systems, in press. DOI: 10.1109/TITS.2023.3265401
[4]	Y. Z. Jin, N. Feng, Y. K. Li, et al., “State estimation for high-speed train based on multirate asynchronous sensors fusion with missing measurements,” IEEE Sensors Journal, vol. 23, no. 5, pp. 4486–4499, 2023. doi: 10.1109/JSEN.2022.3179574
[5]	X. X. Hu, Y. Cao, Y. K. Sun, et al., “Railway automatic switch stationary contacts wear detection under few-shot occasions,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 14893–14907, 2022. doi: 10.1109/TITS.2021.3135006
[6]	H. T. Chen, B. Jiang, S. X. Ding, et al., “Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 1700–1716, 2022. doi: 10.1109/TITS.2020.3029946
[7]	J. Q. Liu, T. Wen, G. Xie, et al., “Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines,” Transportation Safety and Environment, in press. doi: 10.1093/tse/tdac065
[8]	X. X. Hu, Y. Cao, T. Tang, et al., “Data-driven technology of fault diagnosis in railway point machines: Review and challenges,” Transportation Safety and Environment, vol. 4, no. 4, article no. tdac036, 2022. doi: 10.1093/tse/tdac036
[9]	Z. Zhang and X. He, “Active fault diagnosis for linear systems: Within a signal processing framework,” IEEE Transactions on Instrumentation and Measurement, vol. 71, article no. 3505009, 2022. doi: 10.1109/TIM.2022.3150889
[10]	X. X. Hu, R. Niu, and T. Tao, “Research on entropy based corrective maintenance difficulty estimation of metro signaling,” in Proceedings of 2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand, pp.79–85, 2019.
[11]	T. Wen, D. Y. Dong, Q. Y. Chen, et al., “Maximal information coefficient-based two-stage feature selection method for railway condition monitoring,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 7, pp. 2681–2690, 2019. doi: 10.1109/TITS.2018.2881284
[12]	G. Xie, L. L. Sun, T. Wen, et al., “Adaptive transition probability matrix-based parallel IMM algorithm,” IEEE Transactions on Systems, Man, and Cybernetics:Systems, vol. 51, no. 5, pp. 2980–2989, 2021. doi: 10.1109/TSMC.2019.2922305
[13]	Y. X. Li, L. X. Mu, and P. Y. Gao, “Particle swarm optimization fractional slope entropy: A new time series complexity indicator for bearing fault diagnosis,” Fractal and Fractional, vol. 6, no. 7, article no. 345, 2022. doi: 10.3390/fractalfract6070345
[14]	Y. X. Li, B. Z. Tang, B. Geng, et al., “Fractional order fuzzy dispersion entropy and its application in bearing fault diagnosis,” Fractal and Fractional, vol. 6, no. 10, article no. 544, 2022. doi: 10.3390/fractalfract6100544
[15]	H. T. Chen and B. Jiang, “A review of fault detection and diagnosis for the traction system in high-speed trains,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 2, pp. 450–465, 2020. doi: 10.1109/TITS.2019.2897583
[16]	J. Sa, Y. Choi, Y. Chung, et al., “Replacement condition detection of railway point machines using an electric current sensor,” Sensors, vol. 17, no. 2, article no. 263, 2017. doi: 10.3390/s17020263
[17]	Q. Y. Chen, G. Nicholson, C. Roberts, et al., “Improved fault diagnosis of railway switch system using energy-based thresholding wavelets (EBTW) and neural networks,” IEEE Transactions on Instrumentation and Measurement, vol. 70, article no. 3503312, 2021. doi: 10.1109/TIM.2020.3029365
[18]	J. Lee, H. Choi, D. Park, et al., “Fault detection and diagnosis of railway point machines by sound analysis,” Sensors, vol. 16, no. 4, article no. 549, 2016. doi: 10.3390/s16040549
[19]	Y. Cao, Y. K. Sun, G. Xie, et al., “A sound-based fault diagnosis method for railway point machines based on two-stage feature selection strategy and ensemble classifier,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 12074–12083, 2022. doi: 10.1109/TITS.2021.3109632
[20]	N. E. Huang, Z. Shen, S. R. Long, et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. doi: 10.1098/rspa.1998.0193
[21]	Z. P. Wang, L. M. Jia, and Y. Qin, “An integrated feature extraction algorithm for condition monitoring of railway point machine,” in Proceedings of 2016 Prognostics and System Health Management Conference, Chengdu, China, pp.1–5, 2016.
[22]	Y. Huang, J. H. Lin, Z. C. Liu, et al., “A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis,” Journal of Sound and Vibration, vol. 444, pp. 216–234, 2019. doi: 10.1016/j.jsv.2018.12.033
[23]	W. J. Wei and X. F. Liu, “Fault diagnosis of S700K switch machine based on EEMD multiscale sample entropy,” Journal of Central South University (Science and Technology), vol. 50, no. 11, pp. 2763–2772, 2019. (in Chinese) doi: 10.11817/j.issn.1672-7207.2019.11.015
[24]	K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, 2014. doi: 10.1109/TSP.2013.2288675
[25]	Z. P. Li, J. L. Chen, Y. Y. Zi, et al., “Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive,” Mechanical Systems and Signal Processing, vol. 85, pp. 512–529, 2017. doi: 10.1016/j.ymssp.2016.08.042
[26]	F. H. Li, R. Li, L. L. Tian, et al., “Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions,” Mechanical Systems and Signal Processing, vol. 116, pp. 462–479, 2019. doi: 10.1016/j.ymssp.2018.06.055
[27]	Y. Choi, O. Atif, J. Lee, et al., “Noise-robust sound-event classification system with texture analysis,” Symmetry, vol. 10, no. 9, article no. 402, 2018. doi: 10.3390/sym10090402