文献类型：期刊文献

英文题名：Reliability analysis of s-out-of-k multicomponent stress-strength system with dependent strength elements based on copula function

作者：Cai, Jing Yang, Jianfeng Zhang, Yongjin

第一作者：Cai, Jing

通信作者：Cai, J[1]

机构：[1]Guizhou Minzu Univ, Sch Data Sci & Engn, Guiyang, Peoples R China;[2]Guizhou Inst Technol, Sch Data Sci, Guiyang, Peoples R China;[3]Anhui Univ Technol, Sch Math & Phys, Maanshan, Peoples R China

第一机构：Guizhou Minzu Univ, Sch Data Sci & Engn, Guiyang, Peoples R China

通信机构：corresponding author), Guizhou Minzu Univ, Sch Data Sci & Engn, Guiyang, Peoples R China.

年份：2023

卷号：20

期号：5

起止页码：9470-9488

外文期刊名：MATHEMATICAL BIOSCIENCES AND ENGINEERING

收录：;EI(收录号：20231513870288);Scopus(收录号：2-s2.0-85151781824);WOS:【SCI-EXPANDED(收录号:WOS:000964600500002)】；

基金：This work was supported by the National Natural Science Foundation of China (11901134, 71901078 and 62162012) , the Science and Technology Support Program of Guizhou (QKHZC2021 YB531) , the Natural Science Research Project of Department of Education of Guizhou Province (QJJ2022015, QJJ2022047) , the Scientific Research Platform Project of Guizhou Minzu University (GZMUSYS [2021] 04) , and the Natural Science Foundation of the Higher Education Institutions of Anhui Province (KJ2021A0386) .

语种：英文

外文关键词：reliability analysis; s-out-of-k system; multicomponent; stress-strength model; copula

摘要：This paper considers the reliability analysis of a multicomponent stress-strength system which has k statistically independent and identically distributed strength components, and each component is constructed by a pair of statistically dependent elements. These elements are exposed to a common random stress, and the dependence among lifetimes of elements is generated by Clayton copula with unknown copula parameter. The system is regarded to be operating only if at least s (1 <= s <= k ) strength variables in the system exceed the random stress. The maximum likelihood estimates (MLE) of unknown parameters and system reliability is established and associated asymptotic confidence interval is constructed using the asymptotic normality property and delta method, and the bootstrap confidence intervals are obtained using the sampling theory. Finally, Monte Carlo simulation is conducted to support the proposed model and methods, and one real data set is analyzed to demonstrate the applicability of our study.