Simulation-based Bayesian optimal ALT designs for model discrimination

Ehab A. Nasir, Rong Pan

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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. Optimal designs are validated by their recovery rates, where the true, data-generating, model is selected under the DIC (Deviance Information Criterion) model selection rule, and by comparing their performance with other test plans. Results show that the proposed optimal design method has the advantage of substantially increasing a test plans ability to distinguish among competing ALT models, thus providing better guidance as to which model is appropriate for the follow-on testing phase in the experiment.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalReliability Engineering and System Safety
StatePublished - Feb 2015


  • Deviance Information Criterion (DIC)
  • Hellinger distance
  • Model selection
  • Non-parametric curve fitting
  • Reliability test plans

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering


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