Graph Convolutional Domain Adversarial Network for Cross-Domain Few-Shot Rolling Bearing Fault Diagnosis

Cross-domain few-shot recognition aims to establish accurate prototypes and design effective domain alignment strategies, which still presents great challenges in fault diagnosis field. To tackle these issues, a novel graph convolutional domain adversarial network (GCDAN) is proposed for cross-domain few-shot fault diagnosis. First, an attentive pooling-based feature fine-tuning strategy is designed to complete feature pre-processing, and a kernel ridge regression is developed to reproject the prototypical feature. Meanwhile, the self-attention mechanism based prototypical feature reprojection alignment module is proposed to construct accurate prototypes. Besides the prototypical feature alignment, the graph convolution network with structure-aware alignment is further designed to build structural feature relationships in the instance graphs, which can effectively achieve inter-domain structural distribution alignment under class imbalance. Extensive experimental results on 2 rolling bearing fault diagnosis cases demonstrate that GCDAN exhibits obvious advantages against state-of-the-art cross-domain few-shot recognition methods.