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Reliability Analysis of Common Cause Failure Multistate System Based on CUGF

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  • Jin-Zhang Jia
  • Zhuang Li
  • Peng Jia
  • Zhi-guo Yang

Abstract

This paper addresses the problem of mixed uncertainty in the reliability analysis of multistate systems under common cause failure conditions. Combining the cloud model theory, universal generation function (UGF) method, and common cause failure theory, the universal generation function method is extended based on a probabilistic cloud model, i.e., the cloud universal generation function (CUGF) analysis method. The cloud model represents the random and cognitive uncertainty of the state probability, i.e., mixed uncertainty. Next, through CUGF, according to the calculation rules of cloud operators, we provide steps to obtain the reliability of a multistate system under independent failure and common cause failure conditions and obtain cloud digital features for reliability. The accuracy and feasibility of the method are verified by a numerical example. This paper solves the problem of reliability analysis of multistate systems with mixed uncertainty in unit state probability information under common cause failure conditions. We integrate system multistate, information uncertainty, and common cause failure for reliability analysis to avoid large errors, more in line with a project’s actual situation. We propose new ideas and methods to process randomness and fuzzy information or data in multistate system reliability analysis.

Suggested Citation

  • Jin-Zhang Jia & Zhuang Li & Peng Jia & Zhi-guo Yang, 2020. "Reliability Analysis of Common Cause Failure Multistate System Based on CUGF," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, May.
  • Handle: RePEc:hin:jnlmpe:4608124
    DOI: 10.1155/2020/4608124
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    Cited by:

    1. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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