基于自适应非线性维纳过程的剩余寿命预测  

Remaining Life-span Prediction Based on Adaptive Nonlinear Wiener Process

作　　者：丁传彪 赵鑫[1] 朱海振 赵惠 张廷亮 DING Chuanbiao;ZHAO Xin;ZHU Haizhen;ZHAO Hui;ZHANG Tingliang(Laboratory of Automation Test System,School of Aeronautical Engineering,Air Force Engineering University,Xi’an 710051,China;Graduate School of Air Force Engineering University,Xi’an 710051,China)

机构地区：[1]空军工程大学航空工程学院自动测试系统实验室,西安710051 [2]空军工程大学研究生院,西安710051

出　　处：《火力与指挥控制》2024年第8期105-113,119,共10页Fire Control & Command Control

摘　　要：作为预测与健康管理的核心技术,准确的剩余寿命预测对于提高系统的安全性与可靠性具有重要意义。在实际工程应用中,同类产品的不同个体之间通常存在差异,其退化路径并不相同。这就导致通过同类产品历史数据所学习到的模型或参数并不能准确拟合新设备的退化过程。为解决这一问题,基于非线性维纳退化模型提出了一种自适应寿命预测方法。建立同时考虑个体不确定性与测量不确定性的自适应非线性维纳退化模型,并通过卡尔曼滤波、期望最大算法与Rauch-Tung-Striebel平滑减弱维纳过程的马尔科夫性,实现参数的在线更新。通过时间-空间变换推导出对应剩余寿命分布概率密度函数的解析表达式。仿真退化数据与C-MAPSS退化数据集被用于实验验证,实验结果表示,所提出的自适应非线性维纳退化模型能够在线更新模型参数,提高预测精度。As a core technology for both prognostics and health management,accurate remaining life-span prediction is of great significance to enhance the safety and reliability of the system.In actual engineering applications,individual differences usually exist among similar products,so that the learned model or parameters can′t accurately fit the degradation process of the new equipment by the historical data of the similar products.To address this problem,an adaptive life-span prediction method based on a nonlinear Wiener degradation model is proposed.an adaptive nonlinear Wiener degradation model is established considering individual uncertainty and measurement uncertainty at the same,and the online upgrade of parameters is realized by Kalman filtering,expectation maximization algorithm,and Rauch-Tung-Striebel to mitigate the Markovian property of the Wiener process.Moreover,the analytical expressions for the distribution probability density function of the remaining lifespan are derived through time-space transformation.Both simulated degradation data and the CMAPSS degradation dataset are employed for experimental validation.The experiment results show that the proposed adaptive nonlinear Wiener degradation model can improve the prediction accuracy by updating the model parameters online.

关 键 词：自适应 维纳过程 非线性 剩余寿命 卡尔曼滤波 期望最大算法 

分 类 号：TJ01[兵器科学与技术—兵器发射理论与技术]