Bearing Fault Diagnosis Based on Multiple Transformation Domain Fusion and Improved Residual Dense Networks

Automatic feature extraction is one of the most advantageous merits of deep neural network (DNN), meanwhile, it is an important part for intelligent bearing fault diagnosis. However, most of fault diagnosis methods based on DNN usually excavate the complex relations from original time sequence signals which only present the fault information in time domain. Convolutional Neural Network (CNN) has demonstrated powerful feature learning capabilities in bearing fault diagnosis and the deeper the diagnosis model is, the better the recognition performance is, which resulted in some problems. In order to enrich the fault information from different views and enhance the discrimination for features learned from diagnosis network, this paper proposed a bearing fault diagnosis method based on multi-domain information fusion and improved residual dense network. The original signal and its transformed signals composed the multi-channel input, which contained more comprehensive information and will benefit the deep learning. Then it designed a residual dense network and introduced the convolution attention mechanism which can discriminate the importance of features further improve the feature extraction capability and efficiency of diagnosis network. Finally, it achieved the fault classification, analyzed the effects of key parameters and compared with other diagnosis to verify the effectiveness by lots of experimental results.

Automated summary

This paper proposes a bearing fault diagnosis method based on multi-domain information fusion and improved residual dense network. By using multi-channel inputs and convolution attention mechanism, the feature extraction capability and efficiency of the diagnosis network are improved. Experimental results validate the effectiveness of the proposed method.