Adaptive update-based digital twin model and dual-domain collaborative lightweight adversarial transfer network for rolling bearing fault diagnosis

Abstract Due to the difference in the distribution of virtual and physical data caused by external influences such as load and rotational speed fluctuations, it is difficult for digital twins (DTs) to perform data augmentation and knowledge transfer, which results in low diagnostic accuracy for rolling bearings. To alleviate this problem, this study introduces an intelligent diagnostic framework that combines an adaptively updated DT with high-fidelity models and a dual-domain collaborative lightweight adversarial transfer network (DCLATN). This framework facilitates information transfer from physical entities to dynamic models, thereby enhancing cross-domain adaptability for fault diagnosis under complex operating conditions. Firstly, a high-fidelity DT model reflecting the physical operating state of the rolling bearing was established through dynamic modeling and structural parameter updating, and the model was then simulated and analyzed. Then, the DCLATN is constructed to minimize the discrepancy between the source and target domains, where the depth-separable convolution layers with weight-sharing mechanism are constructed for feature extraction and the gradient inversion layers are utilized for feature discrimination. The superiority of the proposed method lies in that it effectively eliminates the domain distribution disparities in vibration signals under diverse operating conditions, thereby ensuring the robustness and efficiency of the model in cross-domain fault diagnosis. The experimental results verify that the proposed method surpasses the state-of-the-art bearing fault diagnosis methods.