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Algorithm for estimating reliability confidence bounds of multi-state systems

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  • Ramirez-Marquez, Jose Emmanuel
  • Levitin, Gregory

Abstract

The paper suggests an effective approach for the estimation of reliability confidence bounds based on component reliability and uncertainty data for multi-state systems with binary-capacitated components. The approach presented is based on the implementation of the universal generating function technique. When compared with a pure Monte Carlo simulation approach, the universal generating function (UGF)-based approach is proven to be more effective due to a more precise reliability estimation and a considerably lower computational effort. Examples are given throughout the paper to illustrate the suggested approach.

Suggested Citation

  • Ramirez-Marquez, Jose Emmanuel & Levitin, Gregory, 2008. "Algorithm for estimating reliability confidence bounds of multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1231-1243.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:8:p:1231-1243
    DOI: 10.1016/j.ress.2007.07.003
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    References listed on IDEAS

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    1. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, March.
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    2. Chun Su & Ning Lin & Yequn Fu, 2016. "Multi-state reliability assessment for hydraulic lifting system based on the theory of dynamic Bayesian networks," Journal of Risk and Reliability, , vol. 230(6), pages 533-544, December.
    3. Ge, Qianru & Peng, Hao & van Houtum, Geert-Jan & Adan, Ivo, 2018. "Reliability optimization for series systems under uncertain component failure rates in the design phase," International Journal of Production Economics, Elsevier, vol. 196(C), pages 163-175.
    4. Yu, Huan & Yang, Jun & Peng, Rui & Zhao, Yu, 2016. "Reliability evaluation of linear multi-state consecutively-connected systems constrained by m consecutive and n total gaps," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 35-43.

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