An Imbalanced Fault Diagnosis Method Based on Multi-Sensor Selection and Graph Attention Mechanism

Severe diagnostic errors are often caused by the significant imbalance between normal and fault data in bearing datasets. To solve this challenge, a graph attention convolutional neural network based on sensitivity analysis and correlation analysis (SCGAT) is proposed to achieve bearing fault diagnosis under imbalanced-dataset conditions. Firstly, a graph attention convolutional neural network is constructed to effectively extract fault-related features from multi-sensor data. Then, a sensor sensitivity analysis module is built to filter and select effective sensor information. A sensor correlation analysis module is introduced to distinguish the correlation between different sensors, and strongly correlated sensors are merged. Finally, the merged features are input into a classifier for fault diagnosis. The effectiveness of the proposed method is verified on a power transmission simulation experiment platform. The experimental results show that the proposed SCGAT can effectively achieve fault diagnosis under imbalanced data conditions, and exhibits higher diagnostic accuracy and stability compared to other models.