An Improved Fault Diagnosis Method of Rolling Bearings Based on Multi-Scale Attention CNN

Rolling bearing fault diagnosis based on convolutional neural network is greatly effective for bearing maintenance, and it is of great significance for ensuring the safe operation of rotating machinery. However, the traditional convolutional neural network models only focus on the single-scale feature and ignore the multi-scale deep information, which results in low performance for conducting the complex fault diagnosis problem. Aiming at this problem, we propose an improved fault diagnosis method of rolling bearings based on multi-scale attention convolutional neural network. In the first stage, a one-dimensional convolutional neural network model integrating feature extraction and fault intelligence classification is established. Subsequently, a multi-scale structure with serial layer skipping connection is constructed to extract multi-scale features in different reception fields, and the "Concat" operator is used to fuse the extracted features of each layer with SE attention mechanism in order to obtain more important feature information. And then, the fault samples are input into the network model to realize the end-to-end fault diagnosis of rolling bearings result from the nonlinear fitting ability of deep learning. Extensive experiments on two well-known rolling bearing datasets validate that the proposed method not only achieves higher fault diagnosis accuracy on the fault data sets under constant load conditions, but also exhibits strong fault identification and transfer diagnosis ability on the fault data sets under variable load conditions.