Data-Driven ToMFIR-Based Incipient Fault Detection and Estimation for High-Speed Rail Vehicle Suspension Systems

Fault detection and estimation issues of China railway high-speed (CRH) train suspension systems in early stage are addressed in this article based on data-driven design of total measurable fault information residual (ToMFIR). First, a discrete trailer car model of the CRH train is established. Based on this model, input/output (I/O) data matrices and system data models are constructed step by step using ToMFIR theory through sensor measurements. By utilizing the projection on controller residual, the data-driven form of ToMFIR residual can be further obtained. For the purpose of efficient and accurate incipient fault detection, the Kullback-Leibler divergence (KLD), an indirect method, is employed to evaluate and monitor the slight changes in the ToMFIR residual in matrix form. Finally, a fault amplitude estimation method based on KLD for detecting incipient sensor effectiveness loss is introduced. Simulation results demonstrate that the data-driven detection and estimation scheme proposed offers higher sensitivity to spring faults, damper faults, actuator faults, and sensor faults of CRH train suspension systems in early stage.