Mscon-C: A Model to Integrate Multiscale Convolution and CBAM Attention Mechanism for Bearing Fault Diagnosis

Nowadays, Convolutional Neural Networks (CNNs) are the mainstream model in the bearing fault diagnosis area. Aiming at the problems of “incomplete capture of multiscale fault features and insufficient use of key time and frequency domain information”, a bearing fault diagnosis model combining multiscale convolution and Convolutional Block Attention Module (CBAM) is proposed, referred to as Mscon-C (standing for Multiscale convolution and CBAM) in this paper. Firstly, the time-frequency domain joint features of bearing faults are obtained by continuous wavelet transform. Then, the fine-grained and global fault features are synchronously extracted by multiscale parallel convolution. Finally, the weight of key features is enhanced by CBAM attention. The performance of Mscon-C was verified on two datasets, and its accuracy reached more than 95% on 12 tasks in two datasets, which was significantly improved compared with traditional CNN and Squeeze-and-Excitation-CNN (SE-CNN), and verified the effectiveness of the combination of multiscale parallel convolution and CBAM.