Sequential Bayesian Design for Accelerated Life Tests

ABSTRACT

Most of the recently developed methods on optimum planning for accelerated life tests (ALT) involve “guessing” values of parameters to be estimated, and substituting such guesses in the proposed solution to obtain the final testing plan. In reality, such guesses may be very different from true values of the parameters, leading to inefficient test plans. To address this problem, we propose a sequential Bayesian strategy for planning of ALTs and a Bayesian estimation procedure for updating the parameter estimates sequentially. The proposed approach is motivated by ALT for polymer composite materials, but are generally applicable to a wide range of testing scenarios. Through the proposed sequential Bayesian design, one can efficiently collect data and then make predictions for the field performance. We use extensive simulations to evaluate the properties of the proposed sequential test planning strategy. We compare the proposed method to various traditional non-sequential optimum designs. Our results show that the proposed strategy is more robust and efficient, as compared to existing non-sequential optimum designs. Supplementary materials for this article are available online.

KEYWORDS:

• Accelerated life test
• Bayesian test planning
• Fatigue testing
• MCMC
• Optimum design
• Sequential design

Acknowledgments

The authors would like to thank William Q. Meeker for his helpful comments and suggestions on earlier version of the article. The authors thank the editor, an associate editor, and three referees, for their valuable comments that helped us to improve this article. The authors acknowledge Advanced Research Computing at Virginia Tech for providing computational resources.

Additional information

Funding

The work by Hong was partially supported by the National Science Foundation under Grant CNS-1565314 to Virginia Tech.