Multi-Modal Bearing Fault Diagnosis Based on Multiscale Feature Enhancement and Adaptive Fusion

Abstract

Bearings are critical components of modern large-scale rotating machinery, such as wind turbines, and their operating condition directly affects system safety, reliability, and operation and maintenance costs. Vibration and current signals reflect the mechanical responses and electromechanically coupled responses induced by bearing faults, respectively, and the two modalities differ in their characteristic representations of fault information. When vibration and current features are fused only once at the end of a network, early complementary information cannot be incorporated into subsequent feature learning; moreover, a single-level weighting scheme cannot capture variations in modality contributions across different samples and feature depths. To address these limitations, a Multi-Scale Feature Enhancement and Adaptive Fusion Network (MSFEAF-Net) is developed. The network employs a dual-branch multi-scale residual architecture to extract vibration and current features, enables early cross-modal interaction through bidirectional cross-attention after the first residual stage, and enhances the vibration and current modalities using one-dimensional spatial attention and channel attention, respectively, according to their distinct feature representations. Subsequently, sample-dependent modality weights are generated at three feature levels, and a feature pyramid is used to aggregate feature information across different levels. Finally, parallel multi-scale convolutions and channel–spatial attention are employed to further refine the features for bearing condition identification. Experimental results show that MSFEAF-Net achieves a test accuracy of 99.88% on the PU dataset under conventional operating conditions. Under strong noise at −8 dB, it maintains accuracies of 91.78% and 91.32% on the PU and KAIST datasets, respectively. In the cross-condition task involving simultaneous variations in rotational speed, torque, and radial load, the model achieves an accuracy of 92.45%. These results validate the effectiveness of the proposed method for fault diagnosis under strong noise and complex operating conditions.