基于变点处数据处理的剩余寿命预测  

Remaining useful life prediction based on data processing at change points

作　　者：白晏年 李小波[1] 杨志豪 刘心怡 史尚贤 Bai Yannian;Li Xiaobo;Yang Zhihao;Liu Xinyi;Shi Shangxian(College of Urban Rail Transit,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学城市轨道交通学院,上海201620

出　　处：《电子测量技术》2025年第2期108-114,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(51907117)项目资助。

摘　　要：针对两阶段剩余寿命预测模型变点后初始时刻预测精度较低的问题,提出了一种基于变点处数据处理的剩余寿命预测算法。首先利用维纳过程构建退化模型,采用期望最大化算法与贝叶斯方法相结合实现参数更新;接着对退化数据进行变点识别,确定变点前的部分退化数据用于变点后初始时刻的寿命预测,最后进行了分别用仿真数据与NASA试验数据进行了算法验证。结果表明,该算法进一步提高了剩余寿命的预测精度。通过NASA试验数据的预测结果看,相较于单一阶段寿命预测模型和两阶段寿命预测模型,本文算法的均方根误差分别降低了10.76和1.78,对产品的剩余寿命预测具有重要意义。In view of the problems of low prediction accuracy of the initial moment after the change point of the two-stage residual life prediction model,a remaining useful life prediction algorithm based on the data processing at the change point is proposed.Firstly,the Wiener process was used to construct the degradation model and the expectation maximization algorithm with Bayesian method was used to realize parameter updating.The degraded data were identified at the change point,and part of the degraded data before the change point were determined to be used for the life prediction at the initial moment after the change point to reduce prediction error.Finally,the algorithm was validated using simulation data and NASA test data,respectively.The results show that the prediction accuracy of the proposed algorithm is further improved.According to the prediction results of NASA test data,compared with the single-stage life prediction model and two-stage life prediction model,the root mean square error is reduced by 10.76 and 1.78 respectively,which is of great significance for the prediction of the remaining life of the product.

关 键 词：剩余寿命预测 维纳过程 两阶段模型 变点处数据处理 

分 类 号：TM407[电气工程—电器] TN607[电子电信—电路与系统]