Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 3; pp. 1700 - 1716
• Main Authors: Chen, Hongtian, Jiang, Bin, Ding, Steven X., Huang, Biao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-03-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data-driven; Fault detection; fault detection and diagnosis (FDD); Fault diagnosis; High speed rail; high-speed trains; Intelligent transportation systems; Machine learning; Mathematical model; Railroad transportation; Reliability; Safety; Sensor systems; System identification; Traction; Traction motors; traction systems; Trains
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2020.3029946

Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description of traction systems, have received special attention because of their overwhelming advantages. Based on the existing data-driven FDD methods for traction systems in high-speed trains, the first objective of this paper is to systematically review and categorize most of the mainstream methods. By analyzing the characteristic of observations from sensors equipped in traction systems, great challenges which may prevent successful FDD implementations on practical high-speed trains are then summarized in detail. Benefiting from theoretical developments of data-driven FDD strategies, instructive perspectives on this topic are further elaborately conceived by the integration of model-based FDD issues, system identification techniques, and new machine learning tools, which provide several promising solutions to FDD strategies for traction systems in high-speed trains.