文献类型：期刊文献

英文题名：Remaining Useful Life Prediction Using Bayesian Additive Regression Trees

作者：Wang, Cunjie[1];Qian, Xiyuan[1]

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai, Peoples R China

年份：2026

外文期刊名：INTERNATIONAL JOURNAL OF RELIABILITY QUALITY AND SAFETY ENGINEERING

收录：EI(收录号：20262120755704);WOS:【ESCI(收录号:WOS:001769543900001)】；

语种：英文

外文关键词：Feature importance; turbofan engine health monitoring; shapley additive explanations

摘要：Accurate prediction of the remaining useful life (RUL) is crucial for avoiding unscheduled downtime, enhancing safety, and reducing maintenance costs. Traditional methods face challenges with high-dimensional, nonlinear, and uncertain data. This paper presents a framework based on Bayesian additive regression trees (BART), integrating RUL prediction with feature selection. The model is trained and tested on the NASA CMAPSS dataset, identifying key sensor features through SHAP analysis. Results show that BART can achieve accurate predictions, reasonable uncertainty estimates, and effectively identify critical variables.