A bayes approachto reliability prediction utilizing data from accelerated life test sand field failure observations

Research output: Contribution to journalArticle

46 Scopus citations

Abstract

A Bayes approach is proposed to improve product reliability prediction by integrating failure information from both the field performance data and the accelerated life testing data. It is found that a product's field failure characteristic may not be directly extrapolated from the accelerated life testing results because of the variation of field use condition that cannot be replicated in the lab-test environment. A calibration factor is introduced to model the effect of uncertainty of field stress on product lifetime. It is useful when the field performance of a new product needs to be inferred from its accelerated life test results and this product will be used in the same environment where the field failure data of older products are available. The proposed Bayes approach provides a proper mechanism of fusing information from various sources. The statistical inference procedure is carried out through the Markov chain Monte Carlo method. An example of an electronic device is provided to illustrate the use of the proposed method.

Original languageEnglish (US)
Pages (from-to)229-240
Number of pages12
JournalQuality and Reliability Engineering International
Volume25
Issue number2
DOIs
StatePublished - Mar 1 2009

Keywords

  • Accelerated life testing
  • Bayesian inference
  • Field failure
  • Markov chain monte carlo
  • Reliability prediction

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

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