随机过程-失效机理模型综合的油滤剩余寿命预测OA

Due to the considerable randomness in contaminant size and arrival,as well as unavoidable epistemic uncertainties and manufacturing/installation variations,both parametric and model uncertainties in practice pose a challenge to the oil filter's remaining useful life prediction.The oil filter is a crucial component in guaranteeing hydraulic fluid cleanliness.This paper introduces a remaining useful life prediction method that fuses a physics-based degradation model with data-driven stochastic process models.Based on Bayesian inference,the method achieves effective RUL prediction for oil filters by synthesizing real-time degradation observations with the respective advantages of different candidate models.In comparison to the Ergun and Wiener models,experimental validation shows that the root mean square error(RMSE)of real-time degradation prediction for oil filter achieves 0.003 9 MPa,resulting in drops of 79.9%and 77.5%,respectively.The results indicate that the proposed method exhibits superior prediction accuracy and strong generalization capability,highlighting its practical value for engineering applications.