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Fault Diagnosis of High-speed Train Bogie Based on Deep Neural Network

机译：基于深神经网络的高速列车转向架故障诊断

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摘要

As an important part of high-speed train (HST), the performance of bogie has a direct impact on the safety of train. Real-time monitoring and evaluation of the operating state of bogie are of great significance for the safe operation of the train. The traditional signal processing methods are difficult to analyze the complex vibration signals of bogie which are collected during train operation. Deep Neural Network (DNN) has been widely used in the field of fault diagnosis due to its good performance in feature extraction of complex data. In this paper, DNN is taken as the overall framework. Failure modes include four states: air springs completely fail, anti-yaw dampers completely fail, lateral dampers completely fail and normal operation. In these four states, HST is running at the speed of 200km/h. Using the data collected in one hour of simulation as the input signal of DNN, the diagnostic accuracy of the four working conditions reached 92.5%. Experimental result shows that DNN has a good performance in multi-class fault diagnosis of bogie.
机译：作为高速火车（HST）的重要组成部分，转向架的性能对火车的安全性直接影响。对转向架的运行状态的实时监测和评估对于火车的安全运行具有重要意义。传统的信号处理方法难以分析在火车操作期间收集的转向架的复杂振动信号。由于其在复杂数据的特征提取中的良好性能，深度神经网络（DNN）已被广泛应用于故障诊断领域。在本文中，DNN被视为整体框架。失效模式包括四种状态：气弹完全失效，抗偏航阻尼器完全失效，横向阻尼器完全失效和正常运行。在这四种状态中，HST以200km / h的速度运行。使用在一小时的模拟中收集的数据作为DNN的输入信号，四个工作条件的诊断准确性达到92.5％。实验结果表明，DNN在转向架的多级故障诊断中具有良好的性能。

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