Few-sample Bearing Composite Fault Diagnosis Method Based on Attention and Feature Fusion*

Bearing faults are a common cause of mechanical failures. Composite faults often occur in bearings, which exhibit mutual interference and coupling characteristics. Traditional intelligent diagnosis methods have limitations in extracting effective feature information from composite faults with a small number of samples, leading to low accuracy and poor robustness. This paper presents an intelligent diagnosis method for composite bearing faults with few samples to overcome this limitation. The presented method is an end-to-end neural network model which is based on attention mechanisms and feature fusion. Several experiments have been conducted to demonstrate that the presented intelligent model can effectively diagnose composite bearing faults. Furthermore, the effectiveness of individual blocks of the diagnostic model is demonstrated by the ablation experiments, and the proposed model structure is shown to be more effective in extracting feature information.