Deep Unsupervised Subdomain Adaptation Network for Intelligent Fault Diagnosis: From Simulated Domain to Physical Domain

Unsupervised domain adaptation (UDA) has demonstrated significant success in intelligent mechanical fault diagnosis. However, these methods primarily focus on transferring knowledge from laboratory test rigs or physical entities, which can be costly or unavailable in certain occasions. Moreover, most methods emphasize global distribution alignment while neglecting fine-grained distribution alignment in knowledge transfer, which can lead to misclassification of certain fault categories and subsequently decrease the diagnostic accuracy of the deep model. In response to these issues, a novel simulation-to-real transfer-based deep unsupervised subdomain adaptation method (Sim2Real-DUSDA) is proposed. Taking bearing-fault diagnosis as an example, a high-fidelity 4-DOF dynamic model of rolling bearings is constructed to generate simulation data across various health states and multiple working conditions. The local maximum mean discrepancy (LMMD) is incorporated to achieve a fine-grained distribution alignment between the source and target domains. A deep unsupervised subdomain adaptation transfer learning network is designed to learn knowledge from the sufficiently labeled simulation data and realize fault mode recognition on the unlabeled measured data. Experimental and empirical analyses on three cross-domain diagnostic tasks demonstrate the effectiveness and superiority of the proposed method.