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Optimal burn-in procedure for mixed populations based on the device degradation process history

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  • Cha, Ji Hwan
  • Pulcini, Gianpaolo

Abstract

Burn-in is a method of ‘elimination’ of initial failures (infant mortality). In the conventional burn-in procedures, to burn-in an item means to subject it to a fixed time period of simulated use prior to actual operation. Then, the items which failed during burn-in are just scrapped and only those which survived the burn-in procedure are considered to be of satisfactory quality. Thus, when the items are subject to degradation phenomena, those whose degradation levels at the end of burn-in exceed a given failure threshold level are eliminated. In this paper, we consider a new burn-in procedure for items subject to degradation phenomena and belonging to mixed populations composed of a weak and a strong subpopulation. The new procedure is based on the ‘whole history’ of the degradation process of an item periodically observed during the burn-in and utilizes the information contained in the observed degradation process to assess whether the item belongs to the strong or weak subpopulation. The problem of determining the optimal burn-in parameters is considered and the properties of the optimal parameters are derived. A numerical example is also provided to illustrate the theoretical results obtained in this paper.

Suggested Citation

  • Cha, Ji Hwan & Pulcini, Gianpaolo, 2016. "Optimal burn-in procedure for mixed populations based on the device degradation process history," European Journal of Operational Research, Elsevier, vol. 251(3), pages 988-998.
    • Handle: RePEc:eee:ejores:v:251:y:2016:i:3:p:988-998
    DOI: 10.1016/j.ejor.2015.12.019
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    References listed on IDEAS

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    1. Ye, Zhi-Sheng & Shen, Yan & Xie, Min, 2012. "Degradation-based burn-in with preventive maintenance," European Journal of Operational Research, Elsevier, vol. 221(2), pages 360-367.
    2. Cha, Ji Hwan & Finkelstein, Maxim, 2010. "Burn-in by environmental shocks for two ordered subpopulations," European Journal of Operational Research, Elsevier, vol. 206(1), pages 111-117, October.
    3. Kim, Kyungmee O., 2011. "Burn-in considering yield loss and reliability gain for integrated circuits," European Journal of Operational Research, Elsevier, vol. 212(2), pages 337-344, July.
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    10. Maxim Finkelstein & Ji Hwan Cha, 2013. "Shocks as Burn-in," Springer Series in Reliability Engineering, in: Stochastic Modeling for Reliability, edition 127, chapter 0, pages 313-361, Springer.
    11. Cha, Ji Hwan & Finkelstein, Maxim, 2011. "Burn-in and the performance quality measures in heterogeneous populations," European Journal of Operational Research, Elsevier, vol. 210(2), pages 273-280, April.
    12. Jie Mi, 1996. "Minimizing Some Cost Functions Related to Both Burn-In and Field Use," Operations Research, INFORMS, vol. 44(3), pages 497-500, June.
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    Cited by:

    1. Shen, Jingyuan & Cui, Lirong & Ma, Yizhong, 2019. "Availability and optimal maintenance policy for systems degrading in dynamic environments," European Journal of Operational Research, Elsevier, vol. 276(1), pages 133-143.
    2. Ji Hwan Cha & Maxim Finkelstein, 2022. "A new warranty policy for heterogeneous items subject to monotone degradation processes," Journal of Risk and Reliability, , vol. 236(1), pages 55-65, February.
    3. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    4. Lucianne Varn & Stefanka Chukova & Richard Arnold, 2019. "A stochastic process for modeling failures of a system having a non-monotonic hazard rate function," Journal of Risk and Reliability, , vol. 233(5), pages 731-746, October.

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