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Multi-state system importance analysis method of fuzzy Bayesian networks

Author

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  • Rui-Jun Zhang
  • Lu-Lu Zhang
  • Ming-Xiao Dong

Abstract

In order to quantify the reliability index of the real system and identify the key events affecting the reliability of the system, fuzzy importance analysis method which can be applied to multi-state system is proposed on the basis of Bayesian network targeting the fuzziness and uncertainties of information. The fuzzy set theory is introduced into the Bayesian network analysis. The failure likelihood of the various components of the system is represented by fuzzy subset, and the fault states of components and system are described by fuzzy numbers. Considering the uncertainty of the fault logical relationship among components, the fuzzy conditional probability tables are used to describe the fault logical relationship among components. Two kinds of fuzzy Bayesian network importance are proposed on the basis of fuzzy Bayesian network analysis algorithms, which describe the contribution of various components to system failure clearly. At last, it is proved that the methods are feasible in the important analysis of the accident of crane rope-breaking.

Suggested Citation

  • Rui-Jun Zhang & Lu-Lu Zhang & Ming-Xiao Dong, 2015. "Multi-state system importance analysis method of fuzzy Bayesian networks," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 21(3), pages 395-414.
    • Handle: RePEc:ids:ijisen:v:21:y:2015:i:3:p:395-414
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    Cited by:

    1. Weiguang Zheng & Haonan Jiang & Shande Li & Qiuxiang Ma, 2023. "Reliability Analysis of High-Voltage Drive Motor Systems in Terms of the Polymorphic Bayesian Network," Mathematics, MDPI, vol. 11(10), pages 1-21, May.

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