Simulation-based Bayesian optimal ALT designs for model discrimination

• Published in: Reliability engineering & system safety Vol. 134; pp. 1 - 9
• Main Authors: Nasir, Ehab A., Pan, Rong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2015
• Subjects: Acceleration; Bayesian analysis; Criteria; Design engineering; Deviance Information Criterion (DIC); Hellinger distance; Mathematical models; Model selection; Non-parametric curve fitting; Optimization; Ratings; Reliability test plans; Stresses; Hellinger distance; Non-parametric curve fitting; Reliability test plans; Model selection; Deviance Information Criterion (DIC)
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2014.10.002

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 plan׳s 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. •Planning ALT experiments for acceleration model selection.•A Bayesian optimal design criterion is proposed.•The simulation-based design process is provided.•The validation of the proposed methodology is given through simulations.•The effectiveness of discriminant design is demonstrated by a real-world example.