A new bearing fault diagnosis method based on digital twin-assisted domain adaptation transfer learning

The demand for advanced monitoring and fault diagnosis technologies for critical mechanical components is growing rapidly. Early detection of rolling bearing faults is essential for preventing performance degradation, unplanned downtime, and safety risks. This article presents a novel fault diagnosis method that leverages digital twin technology and transfer learning to address the limitations of existing approaches in terms of data dependency and cross-domain effectiveness. Initially, a precise digital twin model is developed using finite element analysis to accurately simulate bearing dynamics under various operating conditions, generating extensive simulation data. These data compensate for the scarcity of fault data and are valuable for training diagnostic models. To reduce the noise level in real-world data, the snow ablation optimizer algorithm is employed to optimize variational mode decomposition for noise reduction. Subsequently, transfer learning techniques are utilized to treat the simulation data as the source domain and the actual vibration signals as the target domain, enabling domain-adaptive transfer learning. This approach facilitates cross-domain feature alignment and knowledge transfer, further optimized through adversarial loss and the maximum kernel mean discrepancy metric. Moreover, a deep learning model that combines residual convolutional neural networks with a Transformer is developed, significantly enhancing feature extraction and classification accuracy. Experimental validation conducted on the XJTU-SY dataset demonstrates that the proposed diagnostic method exhibits superior diagnostic performance under small sample conditions, outperforming existing diagnostic methods.