Dynamic-static transfer-aware dual attention network for cross-domain diagnosis of compressor components

Multi-source domain adaptation methods have garnered considerable attention in the field of fault diagnosis due to their ability to integrate diverse knowledge from different domains. However, traditional approaches often overlook the heterogeneity among data domains and the imbalance in their respective contributions. Typically, these methods rely on static transfer strategies to directly align all source and target domain data, which limits the potential of multi-source cross-domain diagnostic learning. To address this issue, a dynamic-static transfer-aware dual attention network (DSTDAN) is proposed in this study, specifically designed for fault diagnosis of core components in compressors. First, a dynamic transfer strategy is developed, in which online adaptive parameter updates are employed to finely align feature distributions across domains. Second, to account for intrinsic domain-specific differences, a static transfer strategy is introduced to enable robust cross-domain knowledge extraction, complementing the dynamic strategy and jointly forming a dynamic-static matching module. Finally, a dual-classifier adversarial training mechanism enhanced with an attention mechanism is constructed. Through multi-task collaborative learning, domain contributions to the diagnostic task are automatically weighted, effectively mitigating negative transfer and improving overall diagnostic performance. Extensive experiments conducted on rolling bearing dataset demonstrate that DSTDAN significantly outperforms existing mainstream transfer approaches.