北京航空航天大学学报2026，Vol.52Issue(8)：2738-2747,10.DOI:10.13700/j.bh.1001-5965.2025.0706
随机过程-失效机理模型综合的油滤剩余寿命预测
RUL prediction of oil filter based on random process-failure mechanism model integration
摘要
Abstract
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.关键词
油滤/剩余寿命预测/贝叶斯模型综合/失效机理/维纳过程Key words
oil filter/remaining useful life prediction/Bayesian model integration/failure mechanisms/Wiener process分类
机械制造引用本文复制引用
王伟杰,郭丁珲,耿艺璇..随机过程-失效机理模型综合的油滤剩余寿命预测[J].北京航空航天大学学报,2026,52(8):2738-2747,10.基金项目
国家自然科学基金(52205065) （52205065）
山西省基础研究计划(202403011212005) National Natural Science Foundation of China(52205065) （202403011212005）
Fundamental Research Program of Shanxi Province(202403011212005) （202403011212005）
