摘要：

牵引逆变器是列车动力系统的核心装置, 其功率器件绝缘栅双极性晶体管(IGBT)在长期振动和复杂工况下容易出现随机的间歇开路现象, 该类故障往往在停机后消失, 难以及时被检测。首先, 建立包含牵引供电系统、逆变器及电机的仿真模型分析故障机理, 考虑多电机同步控制下的耦合特性, 对不同管子发生间歇开路时的电流波形进行分析得出: 低概率故障时电流波动幅度较小, 具有一定隐蔽性; 高概率故障表现为电流波形大幅畸变, 并可能引发相邻逆变器的异常, 呈现明显传播性。然后, 针对地铁列车牵引逆变器中IGBT间歇开路故障的隐蔽性和传播性, 提出一种内涵因果分析的故障诊断方法Causal-Res, 利用时间卷积网络(TCN)中的因果卷积机制, 从输出电流信号中提取因果特征向量, 再结合残差神经网络(ResNet)的深层特征学习能力, 对故障特征向量进行分类, 实现间歇开路故障的诊断与定位。最后, 依托基于地铁列车架控牵引系统拓扑结构搭建的小功率试验平台的试验结果表明, 提出的方法在IGBT低概率和高概率发生间歇开路故障的场景下定位故障IGBT的准确率分别为99.99%和99.95%, 试验结果也说明了因果关系的引入能有效提高诊断方法的准确率和稳定性。

关键词: 故障诊断, 绝缘栅双极性晶体管, 间歇开路故障, 因果卷积, 残差神经网络

Abstract:

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.

Key words: fault diagnosis, Insulated Gate Bipolar Transistor (IGBT), intermittent open-circuit fault, causal convolution, Residual Neural Network (ResNet)