Remaining useful life prediction model of cross-domain rolling bearing via dynamic hybrid domain adaptation and attention contrastive learning

Performance degradation and remaining useful life (RUL) prediction are of great significance in improving the reliability of mechanical equipment. Existing cross-domain RUL prediction methods usually reduce data distribution discrepancy by domain adaptation, to overcome domain shift under cross-domain conditions. However, the fine-grained information between cross-domain degradation features and the specific characteristics of the target domain are often ignored, which limits the prediction performance. Aiming at these issues, a RUL prediction method based on dynamic hybrid domain adaptation (DHDA) and attention contrastive learning (A-CL) is proposed for the cross-domain rolling bearings. In the DHDA module, the conditional distribution alignment is achieved by the designed pseudo-label-guided domain adversarial network, and is assigned with a dynamic penalty term to dynamically adjust the conditional distribution when aligning the joint distribution, for improving the fine-grainedness of domain adaptation. The A-CL module aims to help the prediction model actively extract the degradation information of the target domain, to generate the degradation features that match the characteristics of the target domain, for improving the robustness of RUL prediction. Then, the proposed method is verified by the ablation and comparison experiments conducted on PHM2012 and XJTU-SY datasets. The results show that the proposed method performs high accuracy for cross-domain RUL prediction with good generalization performance under three different cross-domain scenarios. •A dynamic hybrid domain adaptation model is proposed to obtain good domain adaptation.•An attention contrastive learning model is designed to get degradation information.•A cross-domain prediction model is proposed for rolling bearing.•The effectiveness of the proposed method is verified by two public bearing datasets.