一种基于GA优化小波LS-SVR的实时寿命预测方法  

Real-Time Lifetime Prediction Method Based on Wavelet LS-SVR Optimized by GA

作　　者：胡友涛[1] 胡昌华[1] 

机构地区：[1]第二炮兵工程学院自动化系,西安710025

出　　处：《南京航空航天大学学报》2011年第B07期203-206,共4页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：国家杰出青年科学基金(61025014)资助项目;国家自然科学基金重点(60736026)资助项目;国家自然科学基金面上(61074072)资助项目

摘　　要：针对性能非线性退化的产品,从研究退化轨迹相似性的角度出发,提出一种基于遗传算法(GA)优化小波最小二乘支持向量回归机(LS-SVR)的实时退化轨迹建模和寿命预测方法。该方法根据特定个体与同类产品的Euclid距离确定隶属度权值,加权小波LS-SVR建立的同类产品退化模型得到特定个体的退化轨迹模型,结合实测数据更新模型并进行实时寿命预测。实例分析验证了所提方法的有效性。Some products have nonlinear performance degradation paths. As for as the comparability of degradation paths is concerned, a real-time lifetime prediction method is proposed based on wavelet least square support vector regression （LS-SVR） which is optimized by genetic algorithm （GA）. The Euclid distances of the specific individual and the same kind of products are used to determine the degree of membership. The specific individual degradation path model is built by the weighting models of same kind products. It is updated with real-time measurement. The proposed method is applied to fatigue crack growth data. Experimental results verify its validity.

关 键 词：实时寿命预测 性能退化 最小二乘支持向量回归机 小波核函数 遗传算法 

分 类 号：TB114.3[理学—概率论与数理统计]