Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction Systems in High-Speed Trains

CHENG Chao; WANG Weijun; MENG Xiangxi; SHAO Haidong; CHEN Hongtian

doi:10.23919/cje.2022.00.154

Volume 32 Issue 5

Sep. 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2023 > 32(5): 982-991

CHENG Chao, WANG Weijun, MENG Xiangxi, et al., “Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction Systems in High-Speed Trains,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 982-991, 2023, doi: 10.23919/cje.2022.00.154

Citation:

CHENG Chao, WANG Weijun, MENG Xiangxi, et al., “Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction Systems in High-Speed Trains,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 982-991, 2023, doi: 10.23919/cje.2022.00.154

Citation:

PDF( 5674 KB)

Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction Systems in High-Speed Trains

doi: 10.23919/cje.2022.00.154

1.
Department of Computer Science and Technology, Changchun University of Technology, Changchun 130012, China
2.
Department of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
3.
Institute of System Research, China Industrial Control Systems Cyber Emergency Response Team, Beijing 100040, China
4.
Department of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
5.
Department of Chemical and Materials Engineering, University of Alberta, Edmonton AB T6G 2V4, Canada

Funds: This work was supported by the National Natural Science Foundation of China (61903047, U20A20186), the Science and Technology Department of Jilin Province (20210201113GX), and the Education Department of Jilin Province (JJKH20210754KJ)

More Information

Author Bio:
Chao CHENG received the M.E. and Ph.D. degrees from Jilin University, Changchun, China, in 2011 and 2014, respectively. He is currently an Associated Professor with the Changchun University of Technology, Changchun. He has been a Postdoctoral Fellow in process control engineering with the Department of Automation, Tsinghua University, Beijing, China, since 2018.He has also been a Postdoctoral Fellow with the National Engineering Laboratory, CRRC Changchun Railway Vehicles Co., Ltd., China, since 2018. His research interests include dynamic system fault diagnosis and predictive maintenance, wireless sensor network, artificial intelligence, and data-driven method. (Email: chengx415@163.com)

Weijun WANG received the B.S. and M.S. degrees from the School of Computer Science and Engineering, Changchun University of Technology, Changchun, China, in 2018 and 2021, respectively, where he is currently pursuing the Ph.D. degree with the School of Mathematics and Statistics. His main research interests include state estimation, distributed sensor networks, and fault diagnosis

Xiangxi MENG received the Ph.D. degree in School of Computer Science and Engineering, Beihang University. He is currently a Researcher with the Institute of System Research, China Industrial Control Systems Cyber Emergency Response Team. His research interests include industrial control network, industrial internet platform, software test, and fault detection

Haidong SHAO received the B.S. degree in electrical engineering and automation and the Ph.D. degree in vehicle operation engineering from School of Aeronautics, Northwestern Polytechnical University, Xi’an, China, in 2013 and 2018, respectively. He is currently an Associate Professor in College of Mechanical and Vehicle Engineering at Hunan University, Changsha, China. From 2019 to 2021, he was a Postdoctoral Fellow with the Division of Operation and Maintenance Engineering, Luleå University of Technology, Luleå, Sweden. His current research interests include fault diagnosis, intelligent prognosis, maintenance decision, and information fusion

Hongtian CHEN received the B.S. and M.S. degrees in School of Electrical and Automation Engineering from Nanjing Normal University, China, in 2012 and 2015, respectively; and he received the Ph.D. degree in College of Automation Engineering from Nanjing University of Aeronautics and Astronautics, China, in 2019. He had ever been a Visiting Scholar at the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Germany, in 2018. Now he is a Postdoctoral Fellow with the Department of Chemical and Materials Engineering, University of Alberta, Canada. His research interests include process monitoring and fault diagnosis, data mining and analytics, machine leaning, and quantum computation; and their applications in high-speed trains, new energy systems, and industrial processes.Dr. Chen was a recipient of the Grand Prize of Innovation Award of Ministry of Industry and Information Technology of China in 2019, the Excellent Ph.D. Thesis Award of Jiangsu Province in 2020, and the Excellent Doctoral Dissertation Award from Chinese Association of Automation (CAA) in 2020. He currently serves as Associate Editors and Guest Editors of a number of scholarly journals such as IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Artificial Intelligence. (Email: hongtian.chen@ieee.org)
Received Date: 2022-05-29
Accepted Date: 2022-10-08

Available Online: 2023-01-16

Publish Date: 2023-09-05

Abstract

Abstract

Fault detection (FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter (UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space model. The network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation (MAISD) is designed for detecting faults. Verifying the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.
- Fault detection,
- Unscented Kalman filter,
- Traction systems,
- State degradation

FullText(HTML)

References(34)

References

[1]	Y. Cao, Z. C. Wang, F. Liu, et al., “Bio-inspired speed curve optimization and sliding mode tracking control for subway trains,” IEEE Transactions on Vehicular Technology, vol.68, no.7, pp.6331–6342, 2019. doi: 10.1109/TVT.2019.2914936
[2]	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
[3]	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
[4]	H. X. Hu, B. Tang, X. J. Gong, et al., “Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks,” IEEE Transactions on Industrial Informatics, vol.13, no.4, pp.2106–2116, 2017. doi: 10.1109/TII.2017.2683528
[5]	Y. P. Wu, W. D. Jin, Y. Li, et al., “Detecting unexpected faults of high-speed train bogie based on Bayesian deep learning,” IEEE Transactions on Vehicular Technology, vol.70, no.1, pp.158–172, 2021. doi: 10.1109/TVT.2020.3048027
[6]	H. T. Chen, B. Jiang, W. Chen, et al., “Data-driven detection and diagnosis of incipient faults in electrical drives of high-speed trains,” IEEE Transactions on Industrial Electronics, vol.66, no.6, pp.4716–4725, 2019. doi: 10.1109/TIE.2018.2863191
[7]	Y. K. Sun, Y. Cao, G. Xie, et al., “Sound based fault diagnosis for RPMs based on multi-scale fractional permutation entropy and two-scale algorithm,” IEEE Transactions on Vehicular Technology, vol.70, no.11, pp.11184–11192, 2021. doi: 10.1109/TVT.2021.3090419
[8]	Y. L. Chen, Z. B. Tian, C. Roberts, et al., “Reliability and life evaluation of a DC traction power supply system considering load characteristics,” IEEE Transactions on Transportation Electrification, vol.7, no.3, pp.958–968, 2021. doi: 10.1109/TTE.2020.3047512
[9]	X. H. Sun, C. L. Wen, and T. Wen, “Maximum correntropy high-order extended Kalman filter,” Chinese Journal of Electronics, vol.31, no.1, pp.190–198, 2022. doi: 10.1049/cje.2020.00.334
[10]	Y. S. Lu, X. Peng, D. Yang, et al., “Model-agnostic meta-learning with optimal alternative scaling value and its application to industrial soft sensing,” IEEE Transactions on Industrial Informatics, vol.17, no.12, pp.8003–8013, 2021. doi: 10.1109/TII.2021.3058426
[11]	L. Yang, C. L. Wen, and T. Wen, “Multilevel fine fingerprint authentication method for key operating equipment identification in cyber-physical systems,” IEEE Transactions on Industrial Informatics, vol.19, no.2, pp.1217–1226, 2023. doi: 10.1109/TII.2022.3193955
[12]	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
[13]	R. Pandit, D. Infield, and T. Dodwell, “Operational variables for improving industrial wind turbine yaw misalignment early fault detection capabilities using data-driven techniques,” IEEE Transactions on Instrumentation and Measurement, vol.70, article no.2508108, 2021. doi: 10.1109/TIM.2021.3073698
[14]	Y. C. Zhang, Y. Xu, Z. Y. Dong, et al., “Real-time assessment of fault-induced delayed voltage recovery: A probabilistic self-adaptive data-driven method,” IEEE Transactions on Smart Grid, vol.10, no.3, pp.2485–2494, 2019. doi: 10.1109/TSG.2018.2800711
[15]	C. Cheng, J. H. Wang, Z. J. Zhou, et al., “A BRB-based effective fault diagnosis model for high-speed trains running gear systems,” IEEE Transactions on Intelligent Transportation Systems, vol.23, no.1, pp.110–121, 2022. doi: 10.1109/TITS.2020.3008266
[16]	W. Q. Bai, X. M. Yao, H. R. Dong, et al., “Mixed H_/H_∞ fault detection filter design for the dynamics of high speed train,” Science China Information Sciences, vol.60, no.4, article no.048201, 2017. doi: 10.1007/s11432-016-9023-1
[17]	W. Q. Bai, H. R. Dong, X. M. Yao, et al., “Robust fault detection for the dynamics of high-speed train with multi-source finite frequency interference,” ISA Transactions, vol.75, pp.76–87, 2018. doi: 10.1016/j.isatra.2018.01.032
[18]	H. T. Chen, B. Jiang, W. Chen, et al., “Edge computing-aided framework of fault detection for traction control systems in high-speed trains,” IEEE Transactions on Vehicular Technology, vol.69, no.2, pp.1309–1318, 2020. doi: 10.1109/TVT.2019.2957962
[19]	L. B. Cosme, W. M. Caminhas, M. F. S. V. D’Angelo, et al., “A novel fault-prognostic approach based on interacting multiple model filters and fuzzy systems,” IEEE Transactions on Industrial Electronics, vol.66, no.1, pp.519–528, 2019. doi: 10.1109/TIE.2018.2826449
[20]	J. I. Aizpurua, V. M. Catterson, I. F. Abdulhadi, et al., “A model-based hybrid approach for circuit breaker prognostics encompassing dynamic reliability and uncertainty,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.48, no.9, pp.1637–1648, 2018. doi: 10.1109/TSMC.2017.2685346
[21]	D. H. Zhou, H. Q. Ji, X. He, et al., “Fault detection and isolation of the brake cylinder system for electric multiple units,” IEEE Transactions on Control Systems Technology, vol.26, no.5, pp.1744–1757, 2018. doi: 10.1109/TCST.2017.2718979
[22]	T. Wang, L. L. Y. Liang, S. K. Gurumurthy, et al., “Model-based fault detection and isolation in DC microgrids using optimal observers,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol.9, no.5, pp.5613–5630, 2021. doi: 10.1109/JESTPE.2020.3045418
[23]	F. Mwasilu and J. W. Jung, “Enhanced fault-tolerant control of interior PMSMs based on an adaptive EKF for EV traction applications,” IEEE Transactions on Power Electronics, vol.31, no.8, pp.5746–5758, 2016. doi: 10.1109/TPEL.2015.2495240
[24]	M. Corno, G. Panzani, and S. M. Savaresi, “Traction-control-oriented state estimation for motorcycles,” IEEE Transactions on Control Systems Technology, vol.21, no.6, pp.2400–2407, 2013. doi: 10.1109/TCST.2013.2238539
[25]	J. H. Ju, Z. M. Zhao, B. C. Shi, et al., “Motor-oriented discrete state event-driven method for multitime-scale simulation of power traction systems,” IEEE Transactions on Transportation Electrification, vol.7, no.3, pp.1652–1661, 2021. doi: 10.1109/TTE.2021.3053027
[26]	L. Zou, T. Wen, Z. D. Wang, et al., “State estimation for communication-based train control systems with CSMA protocol,” IEEE Transactions on Intelligent Transportation Systems, vol.20, no.3, pp.843–854, 2019. doi: 10.1109/TITS.2018.2835655
[27]	H. T. Chen, Z. Chai, B. Jiang, et al., “Data-driven fault detection for dynamic systems with performance degradation: A unified transfer learning framework,” IEEE Transactions on Instrumentation and Measurement, vol.70, article no.3504712, 2021. doi: 10.1109/TIM.2020.3033943
[28]	T. R. Tsai, W. Y. Sung, Y. L. Lio, et al., “Optimal two-variable accelerated degradation test plan for gamma degradation processes,” IEEE Transactions on Reliability, vol.65, no.1, pp.459–468, 2016. doi: 10.1109/TR.2015.2435774
[29]	X. D. Chen, X. L. Sun, X. S. Si, et al., “Remaining useful life prediction based on an adaptive inverse Gaussian degradation process with measurement errors,” IEEE Access, vol.8, pp.3498–3510, 2020. doi: 10.1109/ACCESS.2019.2961951
[30]	W. W. Peng, Y. F. Li, Y. J. Yang, et al., “Bivariate analysis of incomplete degradation observations based on inverse Gaussian processes and copulas,” IEEE Transactions on Reliability, vol.65, no.2, pp.624–639, 2016. doi: 10.1109/TR.2015.2513038
[31]	P. Wang, R. X. Gao, and W. A. Woyczynski, “Lévy process-based stochastic modeling for machine performance degradation prognosis,” IEEE Transactions on Industrial Electronics, vol.68, no.12, pp.12760–12770, 2021. doi: 10.1109/TIE.2020.3047037
[32]	V. N. Kolokoltsov, “The Lévy-Khintchine type operators with variable Lipschitz continuous coefficients generate linear or nonlinear Markov processes and semigroups,” Probability Theory and Related Fields, vol.151, no.1, pp.95–123, 2011. doi: 10.1007/s00440-010-0293-8
[33]	J. L. Bretagnolle and P. Ouwehand, “The Lévy-Itô decomposition theorem,”arXiv preprint, arXiv: 1506.06624, 2015.
[34]	N. Mukherjee, A. Chattopadhyay, S. Chattopadhyay, et al., “Discrete-wavelet-transform and stockwell-transform-based statistical parameters estimation for fault analysis in grid-connected wind power system,” IEEE Systems Journal, vol.14, no.3, pp.4320–4328, 2020. doi: 10.1109/JSYST.2020.2984132