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Energies
Volume 18
Issue 22
10.3390/en18226073
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Fault Detection of High-Speed Train Traction System Based on Probability-Related Slow Feature Analysis

by
Ruiting Zhang
,
Soon-Hyung Lee
,
Kyung-Min Lee
and
Yong-Sung Choi
*
Department of Electrical Engineering, Dongshin University, Naju 58245, Republic of Korea
*
Author to whom correspondence should be addressed.
Energies 2025, 18(22), 6073; https://doi.org/10.3390/en18226073 (registering DOI)
Submission received: 26 September 2025 / Revised: 3 November 2025 / Accepted: 12 November 2025 / Published: 20 November 2025
(This article belongs to the Special Issue Advances in Electrical Engineering: Intelligent Systems, Modern Algorithms and Advanced Technologies)
Versions Notes

Abstract

As the core subsystem of high-speed trains, the reliable operation of the traction system is critical to ensuring train safety. To enhance fault detection performance, this study proposes a probability-related slow feature analysis (PRSFA) method that leverages the intrinsic characteristics of the traction system. Specifically, Kullback–Leibler divergence is incorporated into the conventional slow feature analysis framework. Based on the slow features extracted from traction system data, the probability distribution distance between offline and online features is further computed to construct detection statistics. The feasibility of the proposed approach is validated using the high-speed train traction system simulation platform developed by Central South University. Compared with the existing SFA, DSFA and DWSFA methods, the results show that the PRSFA method can effectively improve the accuracy and robustness of fault detection.
Keywords: slow feature analysis; Kullback–Leibler Divergence; fault detection; traction system slow feature analysis; Kullback–Leibler Divergence; fault detection; traction system

Share and Cite

MDPI and ACS Style

Zhang, R.; Lee, S.-H.; Lee, K.-M.; Choi, Y.-S. Fault Detection of High-Speed Train Traction System Based on Probability-Related Slow Feature Analysis. Energies 2025, 18, 6073. https://doi.org/10.3390/en18226073

AMA Style

Zhang R, Lee S-H, Lee K-M, Choi Y-S. Fault Detection of High-Speed Train Traction System Based on Probability-Related Slow Feature Analysis. Energies. 2025; 18(22):6073. https://doi.org/10.3390/en18226073

Chicago/Turabian Style

Zhang, Ruiting, Soon-Hyung Lee, Kyung-Min Lee, and Yong-Sung Choi. 2025. "Fault Detection of High-Speed Train Traction System Based on Probability-Related Slow Feature Analysis" Energies 18, no. 22: 6073. https://doi.org/10.3390/en18226073

APA Style

Zhang, R., Lee, S.-H., Lee, K.-M., & Choi, Y.-S. (2025). Fault Detection of High-Speed Train Traction System Based on Probability-Related Slow Feature Analysis. Energies, 18(22), 6073. https://doi.org/10.3390/en18226073

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