Study on Fault Diagnosis for Axlebox Bearing of High Speed EMU Based on Convolutional Neural Network

Abstract

Considering that the traditional intelligent diagnosis methods rely too much on signal processing and expert experience to extract fault features and poor model generalization ability, based on deep learning theory, a convolutional neural network algorithm combined with Softmax classifier is proposed to construct deep convolutional neural network model suitable for fault diagnosis for axlebox bearings of high speed EMU. The model convolutions layer by layer from the measured axlebox vibration signals of high speed trains to achieve adaptive feature extraction and fault recognition. The introduction of batch normalization, regularization and Dropout processing effectively improves the model's recognition accuracy and generalization ability. The experimental results show that the optimized deep learning model can accurately extract fault features, realize accurate recognition of single faults and compound faults, and can maintain better recognition performance on unbalanced data sets, and performance evaluation based on statistical indicators. The superiority of model classification performance is also proved in this paper.