Online Adaptive Fault Diagnosis With Test-Time Domain Adaptation

Cross-domain bearing fault diagnosis algorithms have garnered considerable attention in recent years due to their robust ability to address domain bias. However, prevailing methods often grapple with two key challenges: the absence of privacy preservation (necessitating access to source domain data) and the inability to facilitate real-time predictions (requiring iterative training on complete target domain data). In response to these issues, this article introduces an algorithm designed to adapt a pretrained model to the target domain in an online fashion. Notably, data augmentation is employed for pretraining the source domain model, enhancing the generalization capabilities. Subsequently, self-supervised learning is integrated through weight average updating. Furthermore, a memory bank-based approach is introduced to augment the compactness of features within the same class. Evaluation on several public datasets demonstrates that our model not only effectively enhances the diagnostic accuracy of the source model, but also achieves state-of-the-art results compared to other test-time adaptation methods.