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.

引用

页数：7

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