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Bayesian modeling of multi-state hierarchical systems with multi-level information aggregation

Author

Listed:
  • Li, Mingyang
  • Liu, Jian
  • Li, Jing
  • Uk Kim, Byoung

Abstract

Reliability modeling of multi-state hierarchical systems is challenging because of the complex system structures and imbalanced reliability information available at different system levels. This paper proposes a Bayesian multi-level information aggregation approach to model the reliability of multi-level hierarchical systems by utilizing all available reliability information throughout the system. Cascading failure dependency among components and/or sub-systems at the same level is explicitly considered. The proposed methodology can significantly improve the accuracy of system-level reliability modeling. A case study demonstrates the effectiveness of the proposed methodology.

Suggested Citation

  • Li, Mingyang & Liu, Jian & Li, Jing & Uk Kim, Byoung, 2014. "Bayesian modeling of multi-state hierarchical systems with multi-level information aggregation," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 158-164.
    • Handle: RePEc:eee:reensy:v:124:y:2014:i:c:p:158-164
    DOI: 10.1016/j.ress.2013.12.001
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    References listed on IDEAS

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    1. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, April.
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    3. Sun, Yong & Ma, Lin & Mathew, Joseph & Zhang, Sheng, 2006. "An analytical model for interactive failures," Reliability Engineering and System Safety, Elsevier, vol. 91(5), pages 495-504.
    4. Wilson, Alyson G. & Huzurbazar, Aparna V., 2007. "Bayesian networks for multilevel system reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1413-1420.
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    Citations

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    Cited by:

    1. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2018. "An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 124-135.
    2. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    3. Dongjin Lee & Rong Pan, 2017. "Predictive maintenance of complex system with multi-level reliability structure," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4785-4801, August.
    4. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Chen, Xiaoqian, 2020. "Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part I – Independent systems," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Zheng, Yi-Xuan & Xiahou, Tangfan & Liu, Yu & Xie, Chaoyang, 2021. "Structure function learning of hierarchical multi-state systems with incomplete observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    7. Junyu Guo & Hong-Zhong Huang & Weiwen Peng & Jie Zhou, 2019. "Bayesian information fusion for degradation analysis of deteriorating products with individual heterogeneity," Journal of Risk and Reliability, , vol. 233(4), pages 615-622, August.
    8. Wang, Lizhi & Pan, Rong & Wang, Xiaohong & Fan, Wenhui & Xuan, Jinquan, 2017. "A Bayesian reliability evaluation method with different types of data from multiple sources," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 128-135.
    9. Yontay, Petek & Pan, Rong, 2016. "A computational Bayesian approach to dependency assessment in system reliability," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 104-114.

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