CoUDA: Continual Unsupervised Domain Adaptation for Industrial Fault Diagnosis Under Dynamic Working Conditions

Abstract

Unsupervised domain adaptation (UDA) has recently gained attention in fault diagnosis due to its ability to address domain shift problems arising from changes in working conditions. However, when faced with the continual domain shift problem inherent in real-world industries with dynamic working conditions, UDA often suffers from catastrophic forgetting. To address this challenge, we propose a novel replay-free continual UDA framework, CoUDA, for fault diagnosis under dynamic working conditions. In CoUDA, prototype contrastive learning is employed in source domain pre-training in order to improve the model generalization ability in preparation for the adaptation to the subsequent target domains. Then, source discriminator constraint is employed to ensure that the acquired source domain knowledge serves as an anchor, and source feature knowledge distillation is applied to prevent catastrophic forgetting without replay in sequential target domain adaptation. In addition, for better domain adaptation, local domain alignment and information entropy minimization are utilized to achieve fine-grained domain alignment. Experimental results demonstrate the superiority of the proposed CoUDA in achieving robust fault diagnosis under dynamic working conditions.