一种基于遗传算法优化小波支持向量回归机的实时寿命预测方法

A Real-Time Lifetime Prediction Method Based on Wavelet Support Vector Regression Optimized by GA

针对现有实时寿命预测方法没有充分利用同类产品性能退化数据信息的问题,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)优化小波支持向量回归机(WSVR)的实时退化轨迹建模和寿命预测方法.首先基于GA优化WSVR建立各同类产品的性能退化轨迹模型,然后以特定个体的历史测量时刻向量为基准,计算同类产品的相应退化测量值向量及其与特定个体退化测量值向量的Euclid距离,并根据Euclid距离确定隶属度权值,基于加权思想建立特定个体的退化轨迹模型,最后结合实时测量数据依次更新退化测量值向量、Euclid距离、隶属度权值和退化轨迹模型,实现实时寿命预测.实例分析验证了所提出的方法是有效的.

Aiming at the fact that the present real-time lifetime prediction methods do not take full advantage of the same kind of products＇ performance degradation data,as viewed from the comparability of degradation paths,a real-time lifetime prediction method was proposed on the basis of wavelet support vector regression（WSVR） optimized by genetic algorithm（GA）.Firstly,GA-WSVR is employed to build the same kind of products＇ performance degradation path models.Then the specific individual＇s historical measure time vector is used as the benchmark,the same kind of products＇ corresponded degradation measurement vectors are calculated using GA-WSVR models.The Euclid distances of the specific individual and the same kind of products are used to determine degree of membership,so the individual＇s degradation path model is built on the basis of degree-of-membership weighted method.Finally,the measurement vectors,Euclid distances,degree of membership and degradation path model are updated with real-time measurement data.The proposed method is applied to fatigue crack growth data,the experimental results validate the validity.