A deep transfer network based on dual-task learning for predicting the remaining useful life of rolling bearings

Rolling bearings are essential in rotating machinery, and accurate remaining useful life (RUL) predictions are necessary for effective maintenance and optimal performance. Poor trend of health indicators (HI) and operating condition variations can reduce the reliability and accuracy of RUL predictions.To address these challenges, we propose a deep transfer network based on dual-task learning for predicting the remaining life of rolling bearings (DTLDL-RUL). This framework integrates health status assessment and RUL prediction, leveraging task commonalities and differences to create a robust model, then transfers it to improve generalization in the target domain. Specifically, we first mine spatiotemporal features from vibration signals to generate and classify HI, labeling them for subsequent tasks. Next, we use the shared feature extractors and private residual networks to capture common and specific features of each task, merge them with the multi-gate control networks, and adaptively adjust task weights in the loss function to enhance model adaptability. Finally, the trained model is transferred to the target domain using domain adaptation to extract domain invariant features and consider target-specific features, enhancing generalization. Furthermore, to enhance prediction accuracy, we incorporate physical models as constraints in the loss function, combining data-driven and physical principles to improve model interpretability. Experiments conducted on the 2012 PHM and XJTU-SY datasets demonstrate that the proposed method achieves high accuracy and generalization in RUL predictions.