Generalized autoencoder-based fault detection method for traction systems with performance degradation

Abstract

Fault diagnosis of traction systems is important for the safety operation of high-speed trains. Long-term operation of the trains will degrade the performance of systems, which decreases the fault detection accuracy. To solve this problem, this paper proposes a fault detection method developed by a Generalized Autoencoder (GAE) for systems with performance degradation. The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation. Regardless of the probability distribution, it can handle any data, and the GAE has extremely high sensitivity in anomaly detection. Finally, the effectiveness of this method is verified through the Traction Drive Control System (TDCS) platform. At different performance degradation levels, our method’s experimental results are superior to traditional methods.