Bayes approach in RDT using accelerated and long-term life data |
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Authors: | R. Bri |
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Affiliation: | 1. College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China;2. Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA;1. School of Agricultural, Computational and Environmental Sciences, Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD, Australia;2. School of Agricultural Computational and Environmental Sciences, University of Southern Queensland, Springfield, QLD, Australia;3. Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD, Australia |
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Abstract: | A common problem of reliability demonstration testing (RDT) is the magnitude of total time on test required to demonstrate reliability to the consumer’s satisfaction, particularly in the case of high reliability components. One solution is the use of accelerated life testing (ALT) techniques. Another is to incorporate prior beliefs, engineering experience, or previous data into the testing framework. This may have the effect of reducing the amount of testing required in the RDT in order to reach a decision regarding conformance to the reliability specification. It is in this spirit that the use of a Bayesian approach can, in many cases, significantly reduce the amount of testing required.We demonstrate the use of this approach to estimate the acceleration factor in the Arrhenius reliability model based on long-term data given by a manufacturer of electronic components (EC). Using the Bayes approach we consider failure rate and acceleration factor to vary randomly according to some prior distributions. Bayes approach enables for a given type of technology the optimal choice of test plan for RDT under accelerated conditions when exacting reliability requirements must be met. These requirements are given by a hypothetical consumer by two different ways. The calculation of posterior consumer’s risk is demonstrated in both cases.The test plans are optimum in that they take into account Var{λ|data}, posterior risk, E{λ|data}, Median λ or other percentiles of λ at data observed at the accelerated conditions. The test setup assumes testing of units with time censoring. |
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Keywords: | Bayes analysis Accelerated testing Reliability Censored data Estimation of failure rate Posterior risk |
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