**Abstract**

As the core power unit of high-speed train (HST), the diagnosis of faults in traction motor system has a significant importance on both safety and reliability, which can avoid HST crashes. According to the current problems such as early fault characteristics are not obvious and tight coupling, the reliable model with fault severity analysis and the required diagnosis accuracy cannot be achieved by current techniques. Therefore, it is crucial to evaluate HST reliability through resilience enhancement strategies to ensure it can operate with higher resilience. This chapter proposes a method for evaluating the overall reliability of HST traction motors associated with the idea of system modeling and machine learning techniques. First, a novel fault severity model is proposed suitable for the normal and fault conditions. Then electromagnetic torque energy entropy coding is utilized to extract fault features and construct different feature matrixes. Resilience enhancement strategies with support vector machine models are generated from a novel gray wolf optimizer algorithm. The performance of the proposed work is validated through simulation and experimentation on a fault-testing verification platform for the HST traction system.

**Keywords:** reliability analysis, high-speed train, traction motor, fault severity modeling, coupled faults, resilience enhancement
