Abstract
This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distribution for the scale parameters at the various stress levels and the common shape parameter. Using the approach, Bayes point estimates as well as probability statements for use-stress (and accelerated) life parameters may be inferred from a host of testing scenarios. The inference procedure accommodates both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The inference procedure uses the well-known MCMC (Markov Chain Monte Carlo) methods to derive posterior approximations. The approach is illustrated with an example.
Keywords: Dirichlet distribution; Environmental testing; Step-stress testing
Original language | Undefined/Unknown |
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Pages (from-to) | 140-147 |
Number of pages | 8 |
Journal | Reliability Engineering & System Safety |
Volume | 90 |
Issue number | 2-3 |
DOIs | |
Publication status | Published - 2005 |
Keywords
- academic journal papers
- ZX CWTS 1.00 <= JFIS < 3.00