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A Bayesian approach to degradation-based burn-in optimization for display products exhibiting two-phase degradation patterns

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  • Yuan, Tao
  • Bae, Suk Joo
  • Zhu, Xiaoyan

Abstract

Motivated by the two-phase degradation phenomena observed in light displays (e.g., plasma display panels (PDPs), organic light emitting diodes (OLEDs)), this study proposes a new degradation-based burn-in testing plan for display products exhibiting two-phase degradation patterns. The primary focus of the burn-in test in this study is to eliminate the initial rapid degradation phase, while the major purpose of traditional burn-in tests is to detect and eliminate early failures from weak units. A hierarchical Bayesian bi-exponential model is used to capture two-phase degradation patterns of the burn-in population. Mission reliability and total cost are introduced as planning criteria. The proposed burn-in approach accounts for unit-to-unit variability within the burn-in population, and uncertainty concerning the model parameters, mainly in the hierarchical Bayesian framework. Available pre-burn-in data is conveniently incorporated into the burn-in decision-making procedure. A practical example of PDP degradation data is used to illustrate the proposed methodology. The proposed method is compared to other approaches such as the maximum likelihood method or the change-point regression.

Suggested Citation

  • Yuan, Tao & Bae, Suk Joo & Zhu, Xiaoyan, 2016. "A Bayesian approach to degradation-based burn-in optimization for display products exhibiting two-phase degradation patterns," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 55-63.
    • Handle: RePEc:eee:reensy:v:155:y:2016:i:c:p:55-63
    DOI: 10.1016/j.ress.2016.04.019
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    Cited by:

    1. Sun, Xuxue & Cai, Wenjun & Li, Mingyang, 2021. "A hierarchical modeling approach for degradation data with mixed-type covariates and latent heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Sun, Xuxue & Mraied, Hesham & Cai, Wenjun & Zhang, Qiong & Liang, Guoyuan & Li, Mingyang, 2018. "Bayesian latent degradation performance modeling and quantification of corroding aluminum alloys," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 84-96.
    3. Wang, Pingping & Tang, Yincai & Joo Bae, Suk & He, Yong, 2018. "Bayesian analysis of two-phase degradation data based on change-point Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 244-256.
    4. Baker, Rose, 2019. "New survival distributions that quantify the gain from eliminating flawed components," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 493-501.

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