Just-in-time Learning-aided Nonlinear Fault Detection for Traction Systems of High-speed Trains

Author:

Cheng ChaoORCID,Sun XiuyuanORCID,Shao Junjie,Chen HongtianORCID,Shang ChaoORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Control and Systems Engineering

Reference37 articles.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

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