Dynamic pseudo-label guided adversarial multi-scale graph convolutional network for cross-domain fault diagnosis

Recently, domain adaptation, a form of transfer learning, has been extensively applied to mechanical fault diagnosis across diverse operating conditions to address the challenges of insufficient labeled data and frequent operational state transitions. However, most existing approaches focus solely on the spatial distribution of inter-domain categorical features, neglecting the clustering properties of feature clusters. Moreover, most pseudo-labeling approaches lack a rational screening mechanism based on the quality of the pseudo-labels. To address these challenges, this paper proposes a Dynamic Pseudo-Label Guided Adversarial Multi-Scale Graph Convolutional Network (DPAMGCN) for unsupervised cross-domain fault diagnosis. First, a network architecture is designed by cascading a multi-scale parallel Convolutional Neural Network (CNN) with a multi-receptive-field graph convolutional network (MRF-GCN) to extract features. Second, a multi-objective total-loss function is constructed that integrates geometric loss with three standard loss functions to jointly optimize feature clustering and spatial distribution. Finally, a dynamic threshold-based pseudo-label filtering strategy is proposed that, when combined with a geometric loss, enhances the model's generalization capability. Cross-domain transfer experiments conducted on the University of Ottawa (Ottawa) and Huazhong University of Science and Technology (HUST) benchmark datasets demonstrate that DPAMGCN achieves outstanding cross-domain diagnostic performance under the proposed optimization strategy and pseudo-label screening mechanism.