Parity Space-Based Fault Detection for High-Speed Train Traction Motor Systems Described by T-S Fuzzy Model

This paper focuses on the problem of fault detection (FD) for high-speed train traction motor system subject tomultiple-source disturbances. Aimed at the nonlinear charac-teristics of the traction motor system, the Takagi-Sugeno fuzzy model is used for modeling. For the purpose of the FD, the parity space-based method is adopted. Based on this method, a residual generator is constructed, which can achieves not only completely decoupling of residuals from system states and inputs, but also realize the optimal trade-off between the sensitivity of residual to faults and the robustness to disturbance. In addition, to reduce the burden of online computing, a state observer is introduced to reconstruct the parity equation, so that the parity vector can be obtained through recursive calculation. Finally, the proposed method is verified through simulations results. It is shown that the faults of the traction motor system can be detected effectively through the proposed method.