Adversarial Domain Adaptation Fault Diagnosis Method Based on Self-attention Graph Convolutional Network

Abstract

Intelligent fault diagnosis has made significant progress with the advancements in deep learning and big data. However, the assumption of identical training and testing data distributions often fails in dynamic industrial environments, leading to performance degradation. To address this issue, we propose an Adversarial Domain Adaptation Fault Diagnosis Model Based on Self-attention Graph Convolutional Network (ADA-SAG). The model employs the k-nearest neighbors algorithm to construct graph structures that capture faultinstance relationships across source and target domains. A self-attention enhanced graph convolutional network extracts critical features, while a dual-classifier framework, combined with adversarial learning and maximum mean discrepancy regularization, ensures domain-invariant feature alignment. Experimental results on two benchmark datasets show that the proposed model achieves higher accuracy and robustnesscompared to existing methods, making it suitable for diverseoperating conditions. Ablation studies further validate thecontributions of each component to the overall effectivenessof the model.