Bearing Fault Diagnosis Method Based on Improved Convolutional Neural Network

Abstract

A bearing fault diagnosis method based on an improved convolutional neural network(CNN) is proposed to address the problem of low accuracy of traditional convolutional neural network for rolling bearing fault identification. First, the convolutional kernels of the first convolutional layer of the original network are widened to accommodate the bearing fault signal with one-dimensional input. Then, the number of convolutional kernels is reduced while maintaining the feature extraction capability of the network to ensure the deepening of the network while strongly suppressing the overfitting phenomenon of the network. Finally, the batch normalization(BN) layer is added after the convolutional and fully connected layers of the convolutional neural network to accelerate the convergence of the network and prevent the network from gradient disappearance or gradient explosion. Experimental results demonstrate that the proposed method achieves higher diagnostic accuracy and stability than traditional convolutional neural network.