Bearing Fault Diagnosis Method Based on Attention Mechanism and Multi-Channel Feature Fusion

To address the problems of limited identification accuracy and poor generalization ability of bearing fault diagnosis models, a convolutional neural network model for bearing fault diagnosis based on convolutional block attention module and multi-channel feature fusion (CBAM-MFFCNN) is proposed. The method uses signal processing technology to convert one-dimensional vibration signal into three types of two-dimensional time-frequency images, and constructs a network with multi-channel input to learn the three types of images at the same time. To realize the accurate fault diagnosis of bearings in strong noise environment, the structural parameters of the network are optimized. By adding different degrees of Gaussian white noise to the vibration signal, the convolution kernel size and the step of the first layer of the model are optimized. In order to improve the feature extraction ability and generalization performance of the model, the variable load dataset is constructed for training and testing. Experiments are conducted based on the Case Western Reserve University (CWRU) bearing datasets, the experimental results show that compared with the single channel diagnosis model, CBAM-MFFCNN can not only realize accurate identification of bearing fault, but also achieve 100% identification accuracy in fault degree testing.