High-speed railways have a high demand for safety, but they are complex systems when it comes to fault diagnosis. The failure propagation path is difficult to trace which makes it hard to detect and identify a fault in the traditional way like signal-based methods. In recent years, artificial intelligence methods have been successfully applied in system health diagnosis and prognosis. Fault diagnosis methods based on artificial intelligence methods provide a new inspiration for fault diagnosis in the high-speed railway systems. In this article, the current research status of fault diagnosis was introduced, and the practical application of fault diagnosis methods in high-speed railways was summarized. Then taking the train control system as an example, fault diagnosis based on the artificial intelligence methods was discussed using several case studies; the results proved that the fusion of different methods has the potential to improve the diagnostic accuracy. Finally, the future research direction of fault diagnosis for high-speed railways was proposed.
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