Just-in-time Learning-aided Nonlinear Fault Detection for Traction Systems of High-speed Trains

• Published in: International journal of control, automation, and systems Vol. 21; no. 9; pp. 2797 - 2809
• Main Authors: Cheng, Chao, Sun, Xiuyuan, Shao, Junjie, Chen, Hongtian, Shang, Chao
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.09.2023; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Control; Dynamic characteristics; Engineering; False alarms; Fault detection; Faults; High speed rail; Just in time; Mechatronics; Multivariate statistical analysis; Nonlinear systems; Regular Papers; Robotics; Statistical analysis; Statistical methods; Traction; Trains; 제어계측공학; multi-mode; slow feature analysis; just-in-time learning; traction systems; Incipient faults
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-022-0241-2

Traction systems in high-speed trains exhibit significant dynamic characteristics, which mainly arise from operation-point changes. Most existing fault detection methods provide static data models for global structures, especially for traditional multivariate statistical analysis methods constrained by constant operating points. The symptoms of incipient faults are slight and easily hidden. Despite the moderate effect of incipient faults, they will compromise the overall performance and remaining life of traction systems in the long run. Therefore, a just-in-time slow feature analysis method is proposed in this study. The salient advantages of the proposed method are: 1) It can be applied to dynamic non-linear systems; 2) It can detect incipient faults subject to environments containing noise and unknown disturbances; 3) It mitigates false alarms caused by parameter mutation during mode-switching. A series of experiments are carried out on a traction system platform to verify the effectiveness and superiority of the proposed method.