Fault diagnosis of high-speed train suspension systems using multiscale permutation entropy and linear local tangent space alignment

Abstract

Online health monitoring of railway vehicle suspension systems is of critical importance to guarantee train running safety. The currently reported works on vehicle suspension health monitoring mainly adopt model-based approaches. However, detailed parameters of the vehicle suspension systems, usually, are complicated to be acquired. Moreover, an accurate model cannot be easily obtained due to the nonlinearities of vehicle components and the complexity of suspension systems. Considering the limitations of the model-based approaches, a data-driven method, which combines multiscale permutation entropy and linear local tangent space alignment (MPE-LLTSA), is proposed to diagnose the faults of vehicle suspension systems. To demonstrate the effectiveness and advantages of this method, an MBS model of the China CRH3 train is built to generate the bogie frame’s lateral acceleration containing various secondary suspension faults, and the simulated signals are then introduced to evaluate the MPE-LLTSA method. The evaluation results show that the proposed data-driven approach can accurately identify different types of suspension faults. Finally, the MPE-LLTSA method is further validated using the tracking data of the CRH3 train running on a high-speed railway line in China, and the test results show that the proposed method has the potential to be applied to the field of railway engineering.