DocumentCode
111540
Title
Statistical Inference of a Two-Component Series System With Correlated Log-Normal Lifetime Distribution Under Multiple Type-I Censoring
Author
Tsai-Hung Fan ; Tsung-Ming Hsu
Author_Institution
Grad. Inst. of Stat., Nat. Central Univ., Jhongli, Taiwan
Volume
64
Issue
1
fYear
2015
fDate
Mar-15
Firstpage
376
Lastpage
385
Abstract
In a series system, the system fails if any of the components fails. When the system functions, there may exist correlation among components because they are connected within the same system. In this paper, we consider the reliability analysis of multiple Type-I censored life tests of series systems composed of two components with bivariate log-normal lifetime distributions. The major interest is the inference on the mean lifetimes, and the reliability functions of the system and its components. Given observations of the minimum lifetime of the components of each failed system, location of the MLEs highly relies on the initial values in executing the computation numerically. Alternatively, we apply the Bayesian approach after a re-parametrization of the parameters of interest. A simulation study is conducted which shows that the Bayesian approach provides considerably accurate inference. The proposed approach is successfully applied to a real data set.
Keywords
inference mechanisms; log normal distribution; maximum likelihood estimation; reliability; Bayesian approach; MLE; bivariate log-normal lifetime distributions; correlated log-normal lifetime distribution; maximum likelihood estimates; multiple type-I censored life tests; re-parametrization; reliability analysis; reliability functions; statistical inference; two-component series system; Bayes methods; Correlation; Maximum likelihood estimation; Numerical models; Reliability; Standards; Bivariate log-normal distribution; life test; multiple Type-I censoring; series system;
fLanguage
English
Journal_Title
Reliability, IEEE Transactions on
Publisher
ieee
ISSN
0018-9529
Type
jour
DOI
10.1109/TR.2014.2337813
Filename
6866268
Link To Document