A Fault Diagnosis Method for Rotating Machinery Based on CNN With Mixed Information

Currently, convolutional neural networks (CNNs) have shown great potential in the field of rotating machinery fault diagnosis. To maximize accuracy, the network architecture of novel models has become complex, and the number of parameters has also increased, which makes them not economical in terms of computational efficiency. Experience has shown that there is irreversible information loss in the process of excessive downsampling. This article proposes the CNN with mixed information (MIXCNN), a classification model that is efficient and lightweight for end-to-end fault diagnosis, and the convolutional layers maintain the same size of the output throughout the network. The MIXCNN uses depthwise convolution to increase the ability of discrimination in spatial locations and uses traditional convolution to achieve cross-channel interaction of information; then, the residual connection is introduced to reduce the loss of information on the convolutional layers. By appropriately increasing the depth of the network, MIXCNN pays more attention to features that are differentiated and highly recognizable, and more spatial information is mixed by a larger convolution kernel inside the MIXCNN. Experimental validation using publicly available datasets reveals that the proposed method has higher accuracy and generalization capability than state-of-the-art fault diagnosis methods.