Semi-supervised few-shot fault diagnosis driven by multi-head dynamic graph attention network under speed fluctuations

Although graph neural networks (GNNs) have achieved great success in the field of semi-supervised bearing fault diagnosis recently, most GNN-based studies still face the challenge of insufficient labeled samples. Meanwhile, the signals collected from bearings usually have a time offset due to the alternating speed in the actual working conditions, reducing the applicability of traditional GNN diagnosis methods relying on Euclidean distance. And incomplete use of unlabeled data and insufficient exploitation of neighborhood information further reduce the reliability of fault diagnosis models. To solve these problems, a semi-supervised few-shot fault diagnosis method driven by multi-head dynamic graph attention network (MHDGAT) is proposed in this study. First, dynamic time warping (DTW) similarity is introduced in the construction of K-nearest neighbor graph to solve the problem of local time offset caused by speed change. Second, some pseudo labels generated by smoothness assumption are injected into the raw training set, and more comprehensive information can be learned in the process of model training. Then, the presented MHDGAT model can mine rich fault characteristics from different scales by the multi-head attention and extract the most sensitive features due to the proposed dynamic graph mechanism adaptively. Finally, the fault categories can be obtained by a softmax classifier located at the end of the model. The proposed model has been validated in two case studies, and the experimental results indicate that MHDGAT exerts strong performance even under extremely low-label ratios.