Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network with Residual Connection

Abstract

As the rolling bearing is the most important part of rotating machinery, its fault diagnosis has been a research hotspot. In order to diagnose the faults of rolling bearing under different noisy environments and different load domains, a new method named one-dimensional dilated convolution network with residual connection is proposed in this paper. The proposed method uses the one-dimensional time-domain signals of rolling bearing as input. Zigzag dilated convolution is introduced into convolution neural network, which can effectively improve the receptive field of the convolutional layer. A multi-level residual connection structure with different weight coefficients is constructed, so that the lower layer features of convolution neural network can be transferred to the upper layer, which improves the feature learning ability. Moreover, in order to enhance the useful features and weaken the useless features, we add the attention module Squeeze-and-Excitation (SE) block after each sub-residual structure. By using the rolling bearing datasets, the experimental results show that the proposed method can effectively diagnose faults of rolling bearing under different noisy environments and different load domains. Compared with other methods, the proposed method has higher accuracy.