Abstract
Traction systems in high-speed trains exhibit significant dynamic characteristics, which mainly arise from operation-point changes. Most existing fault detection methods provide static data models for global structures, especially for traditional multivariate statistical analysis methods constrained by constant operating points. The symptoms of incipient faults are slight and easily hidden. Despite the moderate effect of incipient faults, they will compromise the overall performance and remaining life of traction systems in the long run. Therefore, a just-in-time slow feature analysis method is proposed in this study. The salient advantages of the proposed method are: 1) It can be applied to dynamic non-linear systems; 2) It can detect incipient faults subject to environments containing noise and unknown disturbances; 3) It mitigates false alarms caused by parameter mutation during mode-switching. A series of experiments are carried out on a traction system platform to verify the effectiveness and superiority of the proposed method.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
H. Chen, B. Jiang, S. X. Ding, and B. Huang, “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, March 2022.
H. Chen, Z. Chai, B. Jiang, and B. Huang, “Data-driven fault detection for dynamic systems with performance degradation: A unified transfer learning framework,” IEEE Transactions on Instrumentation and Measurement, vol. 70, Article Sequence Number 3504712, pp. 1–12, 2021.
D. Ronanki, S. A. Singh, and S. S. Williamson, “Comprehensive topological overview of rolling stock architectures and recent trends in electric railway traction systems,” IEEE Transactions on Transportation Electrification, vol. 3, no. 3, pp. 724–738, September 2017.
C. Cheng, X. Qiao, B. Zhang, H. Luo, Y. Zhou, and H. Chen, “Multiblock dynamic slow feature analysis-based system monitoring for electrical drives of high-speed trains,” IEEE Transactions on Instrumentation and Measurement, vol. 70, Article Sequence Number 3514310, pp. 1–10, 2021.
S. Liu, B. Jiang, Z. Mao, and S. X. Ding, “Adaptive back-stepping based fault-tolerant control for high-speed trains with actuator faults,” International Journal of Control, Automation, and Systems, vol. 17, pp. 1408–1420, May 2019.
Q. Shen, B. Jiang, and P. Shi, “Active fault-tolerant control against actuator fault and performance analysis of the effect of time delay due to fault diagnosis,” International Journal of Control, Automation, and Systems, vol. 15, pp. 537–546, February 2017.
Z. Hou, R. Chi, and H. Gao, “An overview of dynamic-linearization-based data-driven Control and applications,” IEEE Transactions on Industrial Electronics, vol. 64, no. 5, pp. 4076–4090, May 2017.
Z. Chen and K. E. Haynes, Chinese Railways in the Ea of High-speed, Emerald Group Publishing Limited, Bingley, UK, 2015.
H. 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, February 2020.
Z. Liu, H. Wang, J. Liu, Y. Qin, and D. Peng, “Multitask learning based on lightweight 1DCNN for fault diagnosis of wheelset bearings,” IEEE Transactions on Instrumentation and Measurement, vol. 70, Article Sequence Number 3501711, pp. 1–11, 2021.
C. Yang, Z. Wu, T. Peng, H. Zhu, and C. Yang, “A fractional steepest ascent morlet wavelet transform-based transient fault diagnosis method for traction drive control system,” IEEE Transactions on Transportation Electrification, vol. 7, no. 1, pp. 147–160, March 2021.
B. Gou, X. Ge, S. Wang, X. Feng, J. B. Kuo, and T. G. Habetler, “An open-switch fault diagnosis method for single-phase PWM rectifier using a model-based approach in high-speed railway electrical traction drive system,” IEEE Trans. Power. Electron., vol. 31, no. 5, pp. 3816–3826, May 2016.
K. Zhang, B. Jiang, G. Tao, and F. Chen, “MIMO evolution model-based coupled fault estimation and adaptive control with high-speed train applications,” IEEE Transactions on Control Systems Technology, vol. 26, no. 5, pp. 1552–1566, September 2018.
M. Hamadache and D. Lee, “Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: Application to ball bearing fault detection,” International Journal of Control, Automation, and Systems, vol. 15, pp. 506–517, January 2017.
J. Yang, F. Zhu, X. Wang, and X. Bu, “Robust sliding-mode observer-based sensor fault estimation, actuator fault detection and isolation for uncertain nonlinear systems,” International Journal of Control, Automation, and Systems, vol. 13, pp. 1037–1046, May 2015.
C. Cheng, X. Qiao, H. Luo, G. Wang, W. Teng, and B. Zhang, “Data-driven incipient fault detection and diagnosis for the running gear in high-speed trains,” IEEE Transactions on Vehicular Technology, vol. 69, no. 9, pp. 9566–9576, September 2020.
X. Deng, X. Tian, S. Chen, and C. J. Harris, “Nonlinear process fault diagnosis based on serial principal component analysis,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 3, pp. 560–572, March 2018.
Z. Chen, S. X. Ding, T. Peng, C. Yang, and W. Gui, “Fault detection for non-gaussian processes using generalized canonical correlation analysis and randomized algorithms,” IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1559–1567, February 2018.
H. Luo, H. Zhao, and S. Yin, “Data-driven design of fog-computing-aided process monitoring system for large-scale industrial processes,” IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4631–4641, October 2018.
S. Sun, H. Zhang, Y. Wang and J. Zhang, “Dissipativity-based intermittent fault detection and fault-tolerant control for uncertain switched random nonlinear systems with multiple delays,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 12, pp. 7457–7468, 2022.
Y. Song, Z. Liu, A. Ronnquist, P. Novik, and Z. Liu, “Contact wire irregularity stochastics and effect on high-speed railway pantograph-catenary interactions,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 10, pp. 8196–8206, October 2020.
S. Zhang, Fundamental Application Theory and Engineering Technology for Railway High-speed Trains, Science Press, Beijing, China, 2007.
D. Meng, Y. Jia, J. Du, and F. Yu, “Robust learning controller design for MIMO stochastic discrete-time systems: An H∞-based approach,” International Journal of Adaptive Control and Signal Processing, vol. 25, pp. 653–670, 2011.
Y. Lu, X. Peng, D. Yang, M. Yang, and W. Zhong, “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, December 2021.
D. Meng and K. L. Moore, “Robust iterative learning control for nonrepetitive uncertain systems,” IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 907–913, February 2017.
H. Ji, X. He, J. Shang, and D. Zhou, “Incipient fault detection with smoothing techniques in statistical process monitoring,” Control Engineering Practice, vol. 62, pp. 11–21, May 2017.
A. Romanenko, A. Muetze, and J. Ahola, “Incipient bearing damage monitoring of 940-h variable speed drive system operation,” IEEE Transactions on Energy Conversion, vol. 32, no. 1, pp. 99–110, March 2017.
S. Sun, H. Zhang, C. Liu, and Y. Liu, “Dissipativity-based intermittent fault detection and tolerant control for multiple delayed uncertain switched fuzzy stochastic systems with unmeasurable premise variables,” IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 8766–8780, 2022.
S. Yin, H. Gao, J. Qiu, and O. Kaynak, “Fault detection for nonlinear process with deterministic disturbances: A justin-time learning based data driven method,” IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3649–3657, 2017.
X. Yuan, Z. Ge, B. Huang, Z. Song, and Y. Wang, “Semisupervised JITL framework for nonlinear industrial soft sensing based on locally semisupervised weighted PCR,” IEEE Transactions on Industrial Informatics, vol. 13, no. 2, pp. 532–541, April 2017.
Q. Jiang, X. Yan, H. Yi, and F. Gao, “Data-driven batch-end quality modeling and monitoring based on optimized sparse partial least squares,” IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 4098–4107, May 2020.
C. Shang, F. Yang, X. Gao, X. Huang, J. Suykens, and D. Huang, “Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis,” AIChE Journal, vol. 61, pp. 3666–3682, May 2015.
C. Shang, F. Yang, B. Huang, and D. Huang, “Recursive slow feature analysis for adaptive monitoring of industrial processes,” IEEE Transactions on Industrial Electronics, vol. 65, no. 11, pp. 8895–8905, November 2018.
C. Yang, C. Yang, T. Peng, X. Yang, and W. Gui, “A fault-injection strategy for traction drive control systems,” IEEE Transactions on Industrial Electronics, vol. 64, no. 7, pp. 5719–5727, July 2017.
H. Chen, B. Jiang, N. Lu, and Z. Mao, “Deep PCA based real-time incipient fault detection and diagnosis methodology for electrical drive in high-speed trains,” IEEE Transactions on Vehicular Technology, vol. 67, no. 6, pp. 4819–4830, June 2018.
R. T. Samuel and Y. Cao, “Kernel canonical variate analysis for nonlinear dynamic process monitoring,” IFAC-Papers OnLine, vol. 48, no. 8, pp. 605–610, 2015
H. Chen, L. Li, C. Shang, and B. Huang, “Fault detection for nonlinear dynamic systems with consideration of modeling errors: A data-driven approach,” IEEE Transactions on Cybernetics, vol. 53, no. 7, pp. 4259–4269, 2023.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by National Natural Science Foundation of China (61903047, U20A20186) and Jilin Science and Technology Department (20200401127GX).
Chao Cheng received his Ph.D. degree from Jilin University, Changchun, China, in 2014. He is currently an associated with the Changchun University of Technology, Changchun. He has been a Post-Doctoral Fellow in process control engineering with the Department of Automation, Tsinghua University, Beijing, China, since 2018. He has also been a Post-Doctoral 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 networks, artificial intelligence, and data-driven methods.
Xiuyuan Sun received his B.Eng. degree from the Changchun University of Technology, Changchun, China, in 2019. He is currently working toward an M.Eng. degree in computer science and engineer with the Changchun University of Technology. His research interests include complex system fault diagnosis and data-driven fault detection.
Junjie Shao received his B.E. degree in computer science and technology from Changchun Normal University China, in 2011. He is currently the director of the Data Research Office for the CRRC ChangChun Railway Vehicles CO., LTD. He is serious and responsible and he has strong expansion capabilities. He took the lead in building the company’s data research platform and continued to carry out basic technology research and engineering promotion for application. The research results in its field have reached an industry-leading level. As a leader, he constructed the company’s PHM system, and through continuous optimization of the PHM system, he provided strong support for the company’s market bidding, product design, artificial intelligence for IT operations, and vehicle repairs.
Hongtian Chen received his B.S. and M.S. degrees from the School of Electrical and Automation Engineering from Nanjing Normal University, China, in 2012 and 2015, respectively; and he received a 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 Post-Doctoral 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 learning, 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 the People’s Republic 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.
Chao Shang received his B.Eng. degree in automation and a Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 2011 and 2016, respectively. After working as a Postdoctoral Fellow at Cornell University, he joined the Department of Automation, Tsinghua University in 2018 as an Assistant Professor. His research interests include data-driven modeling, monitoring, diagnosis and optimization with applications to industrial manufacturing processes. Dr. Shang is the recipient of Springer Excellent Doctorate Theses Award, Best Paper Award of 1st International Conference on Industrial Artificial Intelligence, Zijing Scholarship, and Teaching Achievement Award from Tsinghua University.
Rights and permissions
About this article
Cite this article
Cheng, C., Sun, X., Shao, J. et al. Just-in-time Learning-aided Nonlinear Fault Detection for Traction Systems of High-speed Trains. Int. J. Control Autom. Syst. 21, 2797–2809 (2023). https://doi.org/10.1007/s12555-022-0241-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-022-0241-2