A Data-Driven-Based Grounding Fault Location Method for the Auxiliary Power Supply System in an Electric Locomotive

Grounding faults are a common type of fault in train auxiliary power supply systems (APS). Timely identification and localization of these faults are crucial for ensuring the stable operation of electric locomotives and the safety of passengers. Therefore, this paper proposes a fault diagnosis method for grounding faults (GFs) that integrates mechanistic insights with data-driven feature extraction. Firstly, this paper analyzes the mechanisms of grounding faults and summarizes the characteristics of their time-frequency distribution. Then, a Short-Time Fourier Transform (STFT) is employed to derive a frequency signature vector enabling classification into three principal categories. Concurrently, a time series sliding window approach is applied to extract time domain indicators for further subdivision of fault types. Finally, a time-frequency hybrid-driven diagnostic model framework is constructed by integrating the frequency distribution with the spatiotemporal map, and validation is conducted using an experimental platform that replicates system fault scenarios with a hardware-in-the-loop (HIL) simulation and executes the real-time diagnostic frameworks on a DSP diagnostic board card. The results demonstrate that the proposed method can detect and accurately locate grounding faults in real time.