Separable Convolutional Network-Based Fault Diagnosis for High-Speed Train: A Gossip Strategy-Based Optimization Approach

With the rapid development of high-speed train, health monitoring of high-speed train traction power system has gradually become a popular research topic. The traction asynchronous motor, as a key component in the traction power systems, greatly affects the reliability, stability, and safety of high-speed train operation. Normally, when faults occur, the train needs to immediately slow down or even stop to avoid unimaginable losses, resulting in limited fault data. Traditional data-driven fault diagnosis methods may face the local optimum problem during the optimization process when training samples are insufficient. In this study, a novel gossip strategy-based fault diagnosis method is proposed to prevent the local optimum problem, thus improving fault diagnosis performance. The proposed gossip strategy-based fault diagnosis method is validated on the hardware-in-the-loop high-speed train traction control system simulation platform, and the experimental results unequivocally show that the proposed method outperforms other well-known methods.