Bearing Fault Diagnosis Method based on Multi-Sensor Hybrid Feature Fusion

Bearing fault diagnosis is vital for saving invaluable time and cost since it is the most critical components in rotary machines. The feature fusion method has been a effective way to enhance the performance of fault diagnosis. However, how to extract and fuse the complementary fault features from multi-sensor data is still a problem to be solved. This study proposes a multi-sensor hybrid feature fusion network (MSHFFN) to fully mine the complementary fault information from multi-sensor signals for bearing fault diagnosis. In the network, the empirical features calculated by statistical methods and deep features are fused for fault classification, since more comprehensive representation of the bearing's condition will be provided. Besides, A hybrid attention which consists of self attention and mutual attention is proposed innovatively for deep feature extraction. Paderborn bearing data set is used to verify the effectiveness of the MSHFFN. Results show that the diagnostic accuracy of MSHFFN can be up to 99.74%, which is significantly higher than other methods.