Few-shot transfer learning method based on meta-learning and graph convolution network for machinery fault diagnosis

Due to the lack of fault signals and the variability of working conditions in engineering practice, there is still a gap between the conventional deep learning fault diagnosis models and the practical application. Aiming at the problem of few-shot fault diagnosis in variable conditions, we propose a novel few-shot transfer learning method based on meta-learning and graph convolutional network for machinery fault diagnosis. The 2D convolution module is used to extract latent features. Then the extracted features and their labels are combined as the nodes, and the similarity between the nodes is used as the connection relationship between the nodes, so as to realize the construction of the graph sample. Subsequently, graph samples are input into the graph convolutional network to evaluate the similarity and complete the classification of faults. Crucially, the idea of metric-based meta-learning is integrated into the graph convolutional network to set tasks and extraction methods. Finally, the analysis and comparison of the diagnostic accuracy under different sample capacity and transfer conditions were demonstrated. The results show that the method can achieve 97.25% diagnostic accuracy with only a few samples in the scene of variable working conditions.