• 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