Multi-scale convolutional network with channel attention mechanism for rolling bearing fault diagnosis

• A new CNN-based model enhancement method for bearing fault diagnosis: CA-MCNN. • A new multi-scale extraction method based on pooling layers. • Adaptive parallel feature fusion mechanism based on 1-D convolution. In recent years, deep learning has achieved great success in bearing fault diagnosis due to its robust feature learning capabilities. However, in the actual industry, the diagnostic accuracy would be degraded under varying operation conditions or in noisy environments. To enhance the diagnostic performance in industrial applications, a Multi-scale Convolutional Neural Network with Channel Attention (CA-MCNN) is proposed in this paper. In CA-MCNN, the maximum pooling and average pooling layers are used to extract the multi-scale information of the bearing signals, which increases the dimensions of input. The channel attention mechanism is introduced to increase the convolutional layer feature learning ability by adaptively scoring and assigning weights to the learned features. Moreover, the feature parallel fusion mechanism based on 1-D convolution is applied to capture complementary multi-scale information and reduce network complexity. The performance of CA-MCNN is compared with other fault diagnosis models, and experimental results verify that the CA-MCNN achieves the highest diagnosis accuracy under noisy environments and varying working speeds.