Machine Learning-Based Diagnosis Method for Intermittent Open-Circuit Faults Affecting IGBTs in Traction Inverters

A traction inverter is the core device in a train power system, and its power semiconductor, the Insulated Gate Bipolar Transistor (IGBT), is prone to random intermittent open-circuit faults under long-term vibrations and complex operating conditions. Such faults often disappear after shutdown, making timely detection difficult. To investigate the fault mechanism, this study establishes a simulation model incorporating a traction power supply system, inverter, and motor. Considering the coupling characteristics under multi-motor synchronous control, this study analyzes the current waveforms of different transistors with intermittent open-circuit faults. Simulation results indicate that low-probability faults cause relatively small current fluctuations and thus exhibit concealment, whereas high-probability faults result in significant waveform distortion and may induce abnormalities in adjacent inverters, showing obvious propagation. Furthermore, to address the concealment and propagation of IGBT intermittent open-circuit faults in metro train traction inverters, this study proposes a Causal-Res fault diagnosis method based on causal analysis. This method employs the causal convolution mechanism of Temporal Convolutional Networks (TCNs) to extract causal feature vectors from output current signals and combines the deep feature learning capabilities of Residual Neural Networks (ResNets) to classify these feature vectors, thereby achieving effective fault diagnosis and localization. Validations are conducted on a low-power test platform built according to the topology of a metro train distributed traction system. The results demonstrate that the proposed method achieves fault localization accuracies of 99.99% and 99.95% under low- and high-probability intermittent open-circuit scenarios, respectively. Comparative experiments confirm that the introduction of causal relationships effectively enhances the accuracy and stability of the diagnostic method.