A Gearbox Fault Diagnosis Method for Small-Sample Conditions Based on Physics-Informed and Multi-Scale Graph Learning

Existing intelligent fault diagnosis methods ignore the influence of sensors at different positions on the model fault diagnosis performance. Furthermore, the lack of interpretability leads to insufficient reliability of the model fault diagnosis results. Therefore, a physics-informed multi-sensor information fusion method for gearbox fault diagnosis is proposed. The method consists of a physics-informed shallow feature extraction module, a hierarchical multi-scale graph learning module, and an adaptive feature fusion module. The shallow feature extraction module is composed of Laplacian convolution. Multi-scale Laplacian convolution kernels are used to capture multi-frequency and multi-scale feature information, enriching fault representations. The hierarchical multi-scale graph learning module adopts graph convolutional neural networks to conduct deep multi-sensor fault feature extraction for generating high-level features. The adaptive feature fusion module realizes the weighting of important sensor data and the suppression of redundant information through attention scores. This method is validated on two gearbox datasets. The results show that when applied to the SEU dataset, the proposed method achieves a diagnosis accuracy 5.8% higher than that of the state-of-the-art method (MIFNet) under small-sample conditions. In noisy environments, the proposed method achieves an average diagnostic accuracy 1.8% higher than that of the state-of-the-art method (LiConvFormer). This indicates that the proposed method exhibits superior fault diagnosis performance and can effectively handle fault diagnosis tasks under small-sample conditions and in noisy environments.