Bearing Fault Diagnosis Based on Multisensor Information Coupling and Attentional Feature Fusion

The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is very important for its stable operation. The information fusion of multisensor data has been a feasible method to enhance the performance of fault diagnosis. However, how to fuse the joint information from different channels or even different kinds of sensors is still an important challenge. This study proposes a novel multisensor information coupling network (MICN) for bearing fault diagnosis, which handles the signals from the same or different types of sensors, and the deeper features can be extracted from multisensors independently and simultaneously fused layer by layer. Especially, during the multilayer feature fusion process, a novel feature-level information coupling model is developed based on the mutual attention mechanism. Finally, to validate the efficiency of the proposed method, several different experiments are designed, and the results show the validity and superiority.