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Modelling Uncertain Aspects of System Dependability with Survival Signatures

  • Frank P. A. CoolenEmail author
  • Tahani Coolen-Maturi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 307)

Abstract

The survival signature was recently introduced to simplify quantification of reliability for systems and networks. It is based on the structure function, which expresses whether or not a system functions given the status of its components. In this paper, we show how a straightforward generalization of the structure function can provide a suitable tool for scenarios of uncertainty and indeterminacy about functioning of a system for the next task. We embed this generalization into the survival signature, leading to a more flexible tool for quantification of the system reliability and related measures of dependability.

Keywords

State Vector Structure Function Survival Signature System Dependability Probability Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Al-nefaiee, A.H., Coolen, F.P.A.: Nonparametric predictive inference for system failure time based on bounds for the signature. Journal of Risk and Reliability 227, 513–522 (2013)Google Scholar
  2. 2.
    Aslett, L.J.M.: ReliabilityTheory: Tools for structural reliability analysis. R package (2012), www.louisaslett.com
  3. 3.
    Augustin, T., Coolen, F.P.A., de Cooman, G., Troffaes, M.C.M.: Introduction to Imprecise Probabilities. Wiley, Chichester (2014)zbMATHGoogle Scholar
  4. 4.
    Coolen, F.P.A.: Nonparametric prediction of unobserved failure modes. Journal of Risk and Reliability 221, 207–216 (2007)Google Scholar
  5. 5.
    Coolen, F.P.A.: Nonparametric predictive inference. In: Lovric (ed.) International Encyclopedia of Statistical Science, pp. 968–970. Springer (2011), www.npi-statistics.com
  6. 6.
    Coolen, F.P.A., Al-nefaiee, A.H.: Nonparametric predictive inference for failure times of systems with exchangeable components. Journal of Risk and Reliability 226, 262–273 (2012)Google Scholar
  7. 7.
    Coolen, F.P.A., Augustin, T.: Learning from multinomial data: a nonparametric predictive alternative to the Imprecise Dirichlet Model. In: Cozman, et al. (eds.) Proceedings ISIPTA 2005, pp. 125–134 (2005)Google Scholar
  8. 8.
    Coolen, F.P.A., Augustin, T.: A nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories. International Journal of Approximate Reasoning 50, 217–230 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Coolen, F.P.A., Coolen-Maturi, T.: Generalizing the signature to systems with multiple types of components. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) Complex Systems and Dependability. Advances in Intelligent Systems and Computing, vol. 170, pp. 115–130. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Coolen, F.P.A., Coolen-Maturi, T., Al-nefaiee, A.H.: Nonparametric predictive inference for system reliability using the survival signature. Journal of Risk and Reliability, doi:10.1177/1748006X14526390 Google Scholar
  11. 11.
    Coolen, F.P.A., Troffaes, M.C.M., Augustin, T.: Imprecise probability. In: Lovric (ed.) International Encyclopedia of Statistical Science, pp. 645–648. Springer (2011)Google Scholar
  12. 12.
    Coolen, F.P.A., Utkin, L.V.: Imprecise reliability. In: Lovric (ed.) International Encyclopedia of Statistical Science, pp. 649–650. Springer (2011)Google Scholar
  13. 13.
    Coolen-Maturi, T., Coolen, F.P.A.: Unobserved, re-defined, unknown or removed failure modes in competing risks. Journal of Risk and Reliability 225, 461–474 (2011)Google Scholar
  14. 14.
    De Finetti, B.: Theory of Probability. Wiley, New York (1974)zbMATHGoogle Scholar
  15. 15.
    Maturi, T.A., Coolen-Schrijner, P., Coolen, F.P.A.: Nonparametric predictive inference for competing risks. Journal of Risk and Reliability 224, 11–26 (2010)Google Scholar
  16. 16.
    Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Computing Surveys 42(3), article 10 (2010)Google Scholar
  17. 17.
    Samaniego, F.J.: System Signatures and their Applications in Engineering Reliability. Springer (2007)Google Scholar
  18. 18.
    Zamojski, W., Mazurkiewicz, J.: From reliability to system dependability - theory and models. In: Kolowrocki, Soszynska-Budny (eds.) Proceedings SSARS 2011 - 5th Summer Safety & Reliability Seminars, vol. 1, pp. 223–231 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematical SciencesDurham UniversityDurhamUK
  2. 2.Durham University Business SchoolDurham UniversityDurhamUK

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