Uncertainty-Weighted Domain Generalization for Remaining Useful Life Prediction of Rolling Bearings Under Unseen Conditions

Domain adaptation (DA) methods are widely used in rolling bearing remaining useful life (RUL) prediction to align source and target domains. However, DA relies on previously collected target datasets, which are not always available in practical applications. To address this problem, an uncertainty-weighted domain generalization (UWDG) method is proposed for RUL prediction of rolling bearings under unseen conditions. First, a knowledge distillation framework is established to capture internally invariant features, which helps keeping the features from varying with the existence of other domains. Second, correlation alignment (CORAL) is applied to acquire mutually invariant feature distributions across domains, while regularization is employed to increase the disparity between the internally invariant and mutually invariant features. Then, multiple domain-generalization-task losses are weighted by homoscedastic uncertainty, ensuring consistency among separate task outputs. Finally, the effectiveness and superiority of UWDG have been validated through comparative experiments using the experimental datasets.