Dynamic Weighted Slow Feature Analysis-based Fault Detection for Running Gear Systems of High-speed Trains
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12555-023-0059-6.pdf
Reference34 articles.
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3. C. Cheng, Y. Guo, J. h. Wang, H. Chen, and J. Shao, “A unified brb-based framework for real-time health status prediction in high-speed trains,” IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9272–9282, 2022.
4. K. Chen, X. Zhang, K. Shi, Y. Yin, and S. He, “Adaptive event-triggered fault detection filter for a class of conic-type nonlinear hidden semi-markov jump systems,” International Journal of Control, Automation, and Systems, vol. 20, pp. 3573–3583, 2022.
5. H. Chen, B. Jiang, S. 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, 2020.
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