A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction

查看全文 作　　者：LI [1]Juan;DAI [2]Hongde;JING [3]Bo;JIAO [3]Xiaoxuan

机构地区：[1]College of Mathematics and Statistics,Ludong University,Yantai 264025,China;[2]School of Basic Sciences for Aviation,Naval Aviation University,Yantai 264001,China;[3]College of Aeronautics Engineering,Air Force Engineering University,Xi’an 710038,China

出　　处：《Chinese Journal of Electronics》索引2021年第30卷第1期,共9页

基　　金：supported by the Shandong Natural Science Foundation of China(No.ZR2017MF036);Defense Science and Technology Project Foundation of China(No.2019-JCJQ-JJ-059)。 摘　　要：Feature extraction plays an important role in Remaining useful life(RUL)prediction.Feature extraction mainly depends on the performance degradation signal in the previous study,in which the dynamic correlations among different signals are ignored,and the RUL accuracy is affected.A new dynamic feature based on the correlations of the performance degradation signal is proposed.First,dynamic correlation coefficients are calculated by copula function as the multivariate correlation performance degradation features.Second,the random effect Wiener process is used for RUL prediction based on the new features,and the maximum likelihood estimation is adopted to calculate the unknown parameters of the Wiener process.Finally,the RUL estimation for solder joints under vibration load is carried out compared with the quantile and quantile-Principal component analysis(PCA)mixed feature extraction method.The research results show that the proposed method improved the prediction accuracy of RUL. 关键词：Remaining useful life Dynamic copula Random effect Wiener-process Solder joints

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A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction

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