Accelerated life test data analysis for repairable systems

Young Yun Won, Suk Kim Eun, Hwan Cha Ji

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In this article, the imperfect maintenance model and proportional intensity model are used to analyze failure data of repairable systems in accelerated life testing. In the design and development phase of products, we should collect and analyze failure data quickly with small proto-type products. Thus, we test the products under accelerated conditions and if the products fail, then we repair and use those continuously in the life testing. Acceleration and repair models are needed to analyze the failure data. An age reduction model (Brown et al.'s Brown et al. 1983, model) and relationship between scale parameter and stress level are assumed. The stress acts multiplicatively on the baseline cumulative intensity. The maximum likelihood method is used, the log-likelihood function is derived, and a maximizing procedure is proposed. In simulation studies, we investigate the accuracy and trends of the maximum likelihood estimator.

Original languageEnglish
Pages (from-to)1803-1814
Number of pages12
JournalCommunications in Statistics - Theory and Methods
Volume35
Issue number10
DOIs
StatePublished - 1 Oct 2006

Keywords

  • Accelerated life test
  • BMS's model
  • Genetic algorithm
  • Log-likelihood
  • Maximum likelihood estimation
  • Repairable system

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