Bayesian planning of step-stress accelerated degradation tests under various optimality criteria

Xiujie Zhao, Rong Pan, Min Xie

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Step-stress accelerated degradation testing (SSADT) has become a common approach to predicting lifetime for highly reliable products that are unlikely to fail in a reasonable time under use conditions or even elevated stress conditions. In literature, the planning of SSADT has been widely investigated for stochastic degradation processes, such as Wiener processes and gamma processes. In this paper, we model the optimal SSADT planning problem from a Bayesian perspective and optimize test plans by determining both stress levels and the allocation of inspections. Large-sample approximation is used to derive the asymptotic Bayesian utility functions under 3 planning criteria. A revisited LED lamp example is presented to illustrate our method. The comparison with optimal plans from previous studies demonstrates the necessity of considering the stress levels and inspection allocations simultaneously.

Original languageEnglish (US)
JournalApplied Stochastic Models in Business and Industry
DOIs
StateAccepted/In press - Jan 1 2018

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Keywords

  • Bayesian inference
  • Degradation modeling
  • Optimal design
  • Optimality criteria
  • Reliability prediction

ASJC Scopus subject areas

  • Modeling and Simulation
  • Business, Management and Accounting(all)
  • Management Science and Operations Research

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