Multiple-Model-Based Diagnosis of Multiple Faults With High-Speed Train Applications Using Second-Level Adaptation

Due to the time-varying characteristics and the interacted nature of multiple faults in high speed train (HST), the fault modeling, isolation and severity estimation cannot be described accurately using a single model, which may result in poor performance of conventional fault diagnosis methods. This paper introduces the idea of multiple models and second level adaptation techniques to diagnose multiple faults of HST traction motor. First, a reduced model description for multiple faults is given. Then, multiple fault isolation framework is developed to simplify the time-varying fault parameters space segmentation. Based on the decoupled fault set, a fault estimation scheme with second level adaptation is used to provide a reliable alarm priority for different fault scenarios. A case study is performed to verify the effectiveness of the proposed approach.