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Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability

Author

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  • Yan-Feng Li

    (University of Electronic Science and Technology of China)

  • Hong-Zhong Huang

    (University of Electronic Science and Technology of China)

  • Jinhua Mi

    (University of Electronic Science and Technology of China)

  • Weiwen Peng

    (University of Electronic Science and Technology of China)

  • Xiaomeng Han

    (University of Electronic Science and Technology of China)

Abstract

Multi-state components, common cause failures (CCFs) and data uncertainty are the general problems for reliability analysis of complex engineering systems. In this paper, a method incorporating fuzzy probability and Bayesian network (BN) into multi-state systems (MSSs) with CCFs is proposed. In particular, basic theories of multi-state BN and fuzzy probability are developed. Moreover, a model integrating CCFs with BN has also been illustrated. In order to incorporate fuzzy probability into MSSs reliability evaluation considering common parent node generated by CCFs, fuzzy probability has to be translated into accurate probability through defuzzification and normalization methods which are both elaborated. In addition, quantitative analysis based on BN is carried out. In this paper, feed system of boring spindle in computer numerical control machine is analyzed as an example to validate the feasibility of the proposed method. It can improve the ability of BN on reliability evaluation of complex system with uncertainty issues.

Suggested Citation

  • Yan-Feng Li & Hong-Zhong Huang & Jinhua Mi & Weiwen Peng & Xiaomeng Han, 2022. "Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability," Annals of Operations Research, Springer, vol. 311(1), pages 195-209, April.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03247-6
    DOI: 10.1007/s10479-019-03247-6
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    References listed on IDEAS

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    1. Jafary, Bentolhoda & Fiondella, Lance, 2016. "A universal generating function-based multi-state system performance model subject to correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 16-27.
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    4. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.
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    Cited by:

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    2. Sangho Lee & Jeongsub Choi & Youngdoo Son, 2024. "Efficient visibility algorithm for high-frequency time-series: application to fault diagnosis with graph convolutional network," Annals of Operations Research, Springer, vol. 339(1), pages 813-833, August.
    3. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Wang, Ning, 2024. "Algorithms for Bayesian network modeling and reliability inference of complex multistate systems with common cause failure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Zeng, Ying & Huang, Tudi & Li, Yan-Feng & Huang, Hong-Zhong, 2023. "Reliability modeling for power converter in satellite considering periodic phased mission," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. Gao, Shan & Wang, Jinting & Zhang, Jie, 2023. "Reliability analysis of a redundant series system with common cause failures and delayed vacation," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Gao, Shan, 2023. "Reliability analysis and optimization for a redundant system with dependent failures and variable repair rates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 637-659.
    7. Chachra, Aayushi & Kumar, Akshay & Ram, Mangey, 2023. "Intuitionistic fuzzy approach to reliability assessment of multi-state systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 489-503.

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