Data-Driven Incipient Fault Detection and Diagnosis for the Running Gear in High-Speed Trains

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 9; pp. 9566 - 9576
• Main Authors: Cheng, Chao, Qiao, Xinyu, Luo, Hao, Wang, Guijiu, Teng, Wanxiu, Zhang, Bangcheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; belief rule base; Circuit faults; Data-driven fault detection and diagnosis; deep slow feature analysis; Diagnostic systems; Fault detection; Fault diagnosis; Feature extraction; Gears; High speed rail; incipient fault; Interference; Multidimensional data; Predictive models; Principal component analysis; Railroad transportation; Running gear; the running gears
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2020.3002865

Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a data-driven FDD method, namely deep slow feature analysis and belief rule base method (DSFA-BRB), for the running gears of high-speed trains. The method uses two kinds of statistics to perform fault detection on the multi-dimensional data of the running gears. In addition, the characteristics of more accurate data are extracted, which greatly reduces the complexity of constructing a diagnostic and quantitative model. Further, by constructing a BRB model combining expert knowledge and data, it is possible to avoid misjudgment caused by data incompleteness. Compared with the traditional methods, the DSFA-BRB algorithm has better performance in reducing fault alarm probability. Finally, the validity of the algorithm is verified by the actual running gears system.