Towards trustworthy remaining useful life prediction of bearing based on a Bayesian physics-informed neural network

The degradation process of bearings is characterized by pronounced nonlinearity and uncertainty, making it difficult to establish explicit dynamic models that accurately describe their degradation behavior. Moreover, most existing methods provide only deterministic remaining useful life (RUL) estimates and lack effective quantification of predictive uncertainty, thereby limiting their reliability in maintenance planning and operational decision-making. To overcome these limitations, this paper proposes a trustworthy RUL prediction method for bearings based on a Bayesian physics-informed neural network (BPINN). This method effectively integrates the physics-informed architecture with a Bayesian framework, thereby fully leveraging the enhancement effect of physical information on model interpretability. Simultaneously, it utilizes the probabilistic modeling capability of the Bayesian framework to provide uncertainty estimation for RUL prediction. Experimental results indicate that it can provide RUL predictions with associated uncertainty information, which offers additional decision-support information for predictive maintenance.