T-S Fuzzy Data-Driven ToMFIR With Application to Incipient Fault Detection and Isolation for High-Speed Rail Vehicle Suspension Systems

被引:4
|
作者
Wu, Yunkai [1 ]
Su, Yu [2 ]
Wang, Yu-Long [2 ]
Shi, Peng [3 ,4 ]
机构
[1] Jiangsu Univ Sci & Technol, Coll Automat, Zhenjiang 212100, Peoples R China
[2] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China
[3] Univ Adelaide, Sch Elect & Mech Engn, Adelaide, SA 5005, Australia
[4] Obuda Univ, Res & Innovat Ctr, H-1034 Budapest, Hungary
基金
中国国家自然科学基金;
关键词
Rail transportation; Fault detection; Sensors; Monitoring; Data models; Analytical models; Actuators; Data-driven ToMFIR; incipient FDI; !text type='JS']JS[!/text] divergence; generalized LR; high-speed rail vehicle; suspension systems; DYNAMIC PROCESSES; DIAGNOSIS; SCHEME;
D O I
10.1109/TITS.2024.3350918
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper addresses the incipient fault detection and isolation (FDI) problem for high-speed rail vehicle suspension systems and explores further results of data-driven total measurable fault information residual (ToMFIR) with Takagi-Sugeno (T-S) fuzzy dynamical mode. Firstly, the T-S fuzzy model is constructed to represent the global nonlinear dynamics of a China Railway High-speed (CRH) trailer car. Secondly, the data-driven ToMFIR based on T-S fuzzy theory and system identification is designed with the help of system input/output(I/O) data models. Moreover, Jensen-Shannon (JS) divergence based evaluation function is proposed for monitoring the slight changes of data-driven ToMFIR. Furthermore, generalized likelihood ratio (LR) reconstruction method combined with data-driven ToMFIR is designed for incipient isolation of suspension actuator and sensor faults. Finally, simulation studies conducted on SIMPACK-MATLAB/Simulink Co-simulation environment are given to demonstrate the effectiveness of the developed FDI scheme for both slowly developing faults and incipient faults with intermittent characteristics.
引用
收藏
页码:7921 / 7932
页数:12
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