A Copula-based Multivariate Degradation Analysis for Reliability Prediction

被引:0
|
作者
Fang, Guanqi [1 ]
Pan, Rong [1 ]
Hong, Yili [2 ]
机构
[1] Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85281 USA
[2] Virginia Tech, Dept Stat, 250 Drillfield Dr, Blacksburg, VA 24061 USA
来源
2018 ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM (RAMS) | 2018年
关键词
copula function; degradation data analysis; multivariate degradation; reliability prediction; INVERSE GAUSSIAN PROCESS; WIENER-PROCESSES; MODEL; PRODUCTS;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Degradation test often involves multivariate Performance Characteristics (PCs) to be analyzed to make reliability prediction. As a result, the complex dependency structure among PCs needs to be addressed. In this paper, we develop a flexible copula-based multivariate model for analyzing highdimensional degradation process. A two-stage method for parameters estimation is developed as an efficient statistical inference scheme. Finally, a real LED dataset is analyzed by the proposed approach.
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页数:7
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