Fault Diagnosis of Rolling Bearing Based on Convolutional Neural Network of Convolutional Block Attention Module

Abstract

Abstract Aiming at the problem of insufficient comprehensive feature extraction in the fault diagnosis of rolling bearings, this paper proposes a fault diagnosis model of convolutional neural network (CNN) based on Convolutional Block Attention Module (CBAM). The model uses CBAM instead of pooling layer to join the CNN, and then exports to the full connected layer to realize the fault type identification and complete the fault diagnosis of rolling bearing. Experiments show that the accuracy of this method is 99.94%, and comparison with some other methods proves the effectiveness of this method.