Attention-Based Bilinear Feature Fusion Method for Bearing Fault Diagnosis

Abstract

The bearing is the key component of rotating mechanical equipment, so the fault diagnosis of bearings is important to improve the reliability and safety of equipment operation. In recent years, feature fusion method has been extensively explored in the health monitoring and fault diagnosis of bearings. However, almost all the existing feature-fusion-based fault diagnosis methods extract features from different signals independently and concatenating them simply. It will lead to the failure of achieving the expected diagnostic accuracy because the complementary fault information is not fully mined and fused. This article proposes a novel bearing fault diagnosis approach based on mutual attention and bilinear model to address these issues. The features extracted from different input are interactive through mutual attention and are fused by the bilinear model, so the complementary fault features are effectively extracted and fine-grained fused. Experiments are conducted on the Paderborn bearing dataset to verify the effectiveness of the proposed method. Results show that the proposed method can effectively extract complementary fault features from different signals and deeply fuse them, and its diagnosis accuracy is up to 99.86%. Its performance is much better than that of simple concatenation and the feature fusion methods proposed in the reference.