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

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.