Bayesian step stress accelerated degradation testing design: a multi-objective Pareto-optimal approach

Xiaoyang Li, Yuqing Hu, Jiandong Zhou, Xiang Li, Rui Kang

Research output: Contribution to journalArticlepeer-review

28 Citations (SciVal)


Step-stress accelerated degradation testing (SSADT) aims to access the reliability of products in a short time. Bayesian optimal design provides an effective alternative to capture parameters uncertainty, which has been widely employed in SSADT design by optimizing specified utility objective. However, there exist several utility objectives in Bayesian SSADT design; for the engineers, it causes much difficulty to choose the right utility specification with the budget consideration. In this study the problem is formulated as a multi-objective model motivated by the concept of Pareto optimization, which involves three objectives of maximizing the Kullback-Leibler (KL) divergence, minimizing the quadratic loss function of p-quantile lifetime at usage condition, and minimizing the test cost, simultaneously, in which the product degradation path is described by an inverse Gaussian (IG) process. The formulated programming is solved by NSGA-II to generate the Pareto of optimal solutions, which are further optimally reduced to gain a pruned Pareto set by data envelopment analysis (DEA) for engineering practice. The effectiveness of the proposed methodologies and solution method are experimentally illustrated by electrical connector’s SSADT
Original languageEnglish
Pages (from-to)9-17
Number of pages9
JournalReliability Engineering and System Safety
Early online date21 Nov 2017
Publication statusPublished - 01 Mar 2018


  • reliability
  • Bayesian optimal design
  • step stress accelerated degradation testing
  • multi objective programming
  • DEA


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