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Accelerated life test (ALT) planning in Bayesian framework is studied in this paper with a focus of differentiating competing acceleration models, when there is uncertainty as to whether the relationshi

Accelerated life test (ALT) planning in Bayesian framework is studied in this paper with a focus of differentiating competing acceleration models, when there is uncertainty as to whether the relationship between log mean life and the stress variable is linear or exhibits some curvature. The proposed criterion is based on the Hellinger distance measure between predictive distributions. The optimal stress-factor setup and unit allocation are determined at three stress levels subject to test-lab equipment and test-duration constraints.

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Date Created
  • 2015-02-01
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  • Text
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    Identifier
    • Digital object identifier: 10.1016/j.ress.2014.10.002
    • Identifier Type
      International standard serial number
      Identifier Value
      0951-8320
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    • NOTICE: this is the author's version of a work that was accepted for publication in RELIABILITY ENGINEERING & SYSTEM SAFETY. Changes resulting from the publishing process, such as editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in RELIABILITY ENGINEERING & SYSTEM SAFETY, 134, 1-9. DOI: 10.1016/j.ress.2014.10.002, opens in a new window

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    This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.

    Nasir, Ehab A., & Pan, Rong (2015). Simulation-based Bayesian optimal ALT designs for model discrimination. RELIABILITY ENGINEERING & SYSTEM SAFETY, 134, 1-9. http://dx.doi.org/10.1016/j.ress.2014.10.002

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