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A Bayes approach to reliability prediction utilizing data from accelerated life tests and field failure observations
Author(s) -
Pan Rong
Publication year - 2009
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.964
Subject(s) - reliability engineering , accelerated life testing , reliability (semiconductor) , bayes' theorem , field (mathematics) , computer science , product (mathematics) , inference , statistical inference , test data , markov chain monte carlo , calibration , data mining , bayesian probability , statistics , engineering , artificial intelligence , mathematics , power (physics) , physics , geometry , quantum mechanics , weibull distribution , pure mathematics , programming language
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. Copyright © 2008 John Wiley & Sons, Ltd.