Fault diagnosis for high‐speed train axle‐box bearing using simplified shallow information fusion convolutional neural network

Abstract

Axle‐box bearings are one of the most critical mechanical components of the high‐speed train. Vibration signals collected from axle‐box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real‐time requirement of axle‐box bearing fault diagnosis for high‐speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle‐box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion‐convolutional neural network (SSIF‐CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF‐CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one‐dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF‐CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.