Abstract
Traditional accelerated life test plans are typically based on optimizing the C-optimality for minimizing the variance of an interested quantile of the lifetime distribution. These methods often rely on some specified planning values for the model parameters, which are usually unknown prior to the actual tests. The ambiguity of the specified parameters can lead to suboptimal designs for optimizing the reliability performance of interest. In this paper, we propose a sequential design strategy for life test plans based on considering dual objectives. In the early stage of the sequential experiment, we suggest allocating more design locations based on optimizing the D-optimality to quickly gain precision in the estimated model parameters. In the later stage of the experiment, we can allocate more observations based on optimizing the C-optimality to maximize the precision of the estimated quantile of the lifetime distribution. We compare the proposed sequential design strategy with existing test plans considering only a single criterion and illustrate the new method with an example on the fatigue testing of polymer composites.
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Acknowledgements
The authors thank the editors and one referee who provided comments that helped us improve this paper. The authors acknowledge Advanced Research Computing at Virginia Tech for providing computational resources. The work by Hong was partially supported by the National Science Foundation under Grant CNS-1565314 to Virginia Tech.
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Lu, L., Lee, IC., Hong, Y. (2019). Bayesian Sequential Design Based on Dual Objectives for Accelerated Life Tests. In: Lio, Y., Ng, H., Tsai, TR., Chen, DG. (eds) Statistical Quality Technologies. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-20709-0_11
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DOI: https://doi.org/10.1007/978-3-030-20709-0_11
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