Learn Then Adapt: A Novel Test-Time Adaptation Method for Cross-Domain Fault Diagnosis of Rolling Bearings

Cross-domain fault diagnosis enhances the generalization capability of diagnostic models across different operating conditions and machines. Current studies tackle the domain shift problem by adapting the model during training with data from the target domain or multiple source domains. However, a more realistic and less explored scenario is automatically adapting a trained (developed) model at test time (deployment period) using limited normal-condition data. To bridge this research gap, we propose a novel test-time adaptation framework to rapidly and effectively adapt the trained model, which only requires mini-batch test data (normal condition). Specifically, we first transform input signals to informative signal embedding and mitigate its noise with a reconstruction loss. Then, we decompose the signal embedding to the domain-related healthy component and the domain-invariant faulty component to better leverage the normal-condition data. Finally, we adapt the model by re-identifying the normal signals of the target domain during the test stage. Extensive experiments verify the effectiveness of our method, demonstrating performance improvements across public and private datasets.