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Efficient surrogate models for reliability analysis of systems with multiple failure modes

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  • Bichon, Barron J.
  • McFarland, John M.
  • Mahadevan, Sankaran

Abstract

Despite many advances in the field of computational reliability analysis, the efficient estimation of the reliability of a system with multiple failure modes remains a persistent challenge. Various sampling and analytical methods are available, but they typically require accepting a tradeoff between accuracy and computational efficiency. In this work, a surrogate-based approach is presented that simultaneously addresses the issues of accuracy, efficiency, and unimportant failure modes. The method is based on the creation of Gaussian process surrogate models that are required to be locally accurate only in the regions of the component limit states that contribute to system failure. This approach to constructing surrogate models is demonstrated to be both an efficient and accurate method for system-level reliability analysis.

Suggested Citation

  • Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:10:p:1386-1395
    DOI: 10.1016/j.ress.2011.05.008
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    References listed on IDEAS

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    1. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    2. Der Kiureghian, Armen & Song, Junho, 2008. "Multi-scale reliability analysis and updating of complex systems by use of linear programming," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 288-297.
    3. Savage, Gordon J. & Kap Son, Young, 2011. "The set-theory method for systems reliability of structures with degrading components," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 108-116.
    4. Kang, Won-Hee & Song, Junho & Gardoni, Paolo, 2008. "Matrix-based system reliability method and applications to bridge networks," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1584-1593.
    5. Doguc, Ozge & Ramirez-Marquez, Jose Emmanuel, 2009. "A generic method for estimating system reliability using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 542-550.
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