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

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. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.