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Shocks as Burn-in

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Stochastic Modeling for Reliability

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Abstract

As described in the previous chapters, in conventional burn-in, the main parameter of the burn-in procedure is its duration. However, in order to shorten the length of this procedure, burn-in is most often performed in an accelerated environment. This indicates that high environmental stress can be more effective in eliminating weak items from a population. In this case, obviously, the larger values of stress should correspond to the shorter duration of burn-in. By letting the stress to increase, we can end up (as some limit) with very short (negligible) durations, in other words, shocks. In practice, the most common types of shocks as a method of burn-in are “thermal shock” and “physical drop”. In these cases, the item is subjected to a very rapid cold-to-hot, or hot-to-cold, instantaneous thermal change or the item is dropped by a “drop tester” which is specifically designed to drop it without any rotational motion, to ensure the most rigorous impact. In this case, the stress level (to be called shock’s severity) can be a controllable parameter for the corresponding optimization, which in a loose sense is an analogue of the burn-in duration in accelerated burn-in.

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References

  1. Bagdonavicius V, Nikulin M (2009) Statistical models to analyze failure, wear, fatigue, and degradation data with explanatory variables. Commun Stat—Theory Methods 38:3031–3047

    Article  MathSciNet  MATH  Google Scholar 

  2. Barlow RE, Proschan F (1975) Statistical theory of reliability and life testing. Holt, Renerhart & Winston, New York

    Google Scholar 

  3. Beard RE (1959) Note on some mathematical mortality models. In: Woolstenholme GEW, O’Connor M (eds) The lifespan of animals. Little, Brown and Company, Boston, pp 302–311

    Google Scholar 

  4. Block HW, Mi J, Savits TH (1993) Burn-in and mixed populations. J Appl Probab 30:692–702

    Article  MathSciNet  MATH  Google Scholar 

  5. Cha JH (2006) An extended model for optimal burn-in procedures. IEEE Trans Reliab 55:189–198

    Article  Google Scholar 

  6. Cha JH, Finkelstein M (2010) Burn-in by environmental shocks for two ordered subpopulations. Eur J Oper Res 206:111–117

    Article  MathSciNet  MATH  Google Scholar 

  7. Cha JH, Finkelstein M (2011) Burn-in for systems operating in a shock environment. IEEE Trans Reliab 60:721–728

    Article  Google Scholar 

  8. Cha JH, Finkelstein M (2013). Burn-in for heterogeneous populations: How to avoid large risks. Commun Stat—Theory Methods (to appear)

    Google Scholar 

  9. El Karoui N, Gerardi A, Mazliak L (1994) Stochastic control methods in optimal design of life testing. Stoch Process Appl 52:309–328

    Article  MATH  Google Scholar 

  10. Finkelstein M (2008) Failure rate modelling for reliability and risk. Springer, London

    Google Scholar 

  11. Finkelstein M (2009) Understanding the shape of the mixture failure rate (with engineering and demographic applications). Appl Stoch Models Bus Ind 25:643–663

    Article  MathSciNet  MATH  Google Scholar 

  12. Mi J (1996) Minimizing some cost functions related to both burn-in and field use. Oper Res 44:497–500

    Article  MATH  Google Scholar 

  13. Reddy RK, Dietrich DL (1994) A 2-level environmental-stress-screening (ESS) model: a mixed-distribution approach. IEEE Trans Reliab 43:85–90

    Article  Google Scholar 

  14. Vaupel JW, Manton KG, Stallard E (1979) The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 16:439–454

    Article  Google Scholar 

  15. Wu S, Xie M (2007) Classifying weak, and strong components using ROC analysis with application to burn-in. IEEE Trans Reliab 56:552–561

    Article  Google Scholar 

  16. Yan L, English JR (1997) Economic cost modeling of environmental-stress-screening and burn-in. IEEE Trans Reliab 46:275–282

    Article  Google Scholar 

  17. Yang G (2002) Environmental-stress-screening using degradation measurements. IEEE Trans Reliab 51:288–293

    Article  Google Scholar 

Download references

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Correspondence to Maxim Finkelstein .

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Finkelstein, M., Cha, J.H. (2013). Shocks as Burn-in. In: Stochastic Modeling for Reliability. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-5028-2_9

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  • DOI: https://doi.org/10.1007/978-1-4471-5028-2_9

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5027-5

  • Online ISBN: 978-1-4471-5028-2

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