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A nonparametric Bayesian network approach to assessing system reliability at early design stages

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  • Lee, Dongjin
  • Pan, Rong

Abstract

It is important to predict a system’s reliability at its early design stages because modifying design to improve reliability and maintainability at a later time in the system’s lifecycle will be costly and, oftentimes, impossible. However, this early prediction is challenging because of the lack of reliability data and the incomplete knowledge of a complex system’s reliability structure. To tackle this problem, this paper presents a nonparametric Bayesian network approach. Employing nonparametric Bayesian network, the limitation of discrete Bayesian network can be overcome, and it can be used as a useful tool for decision support. The proposed methodology is applied to a case study to demonstrate its prognostic and diagnostic capabilities.

Suggested Citation

  • Lee, Dongjin & Pan, Rong, 2018. "A nonparametric Bayesian network approach to assessing system reliability at early design stages," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 57-66.
    • Handle: RePEc:eee:reensy:v:171:y:2018:i:c:p:57-66
    DOI: 10.1016/j.ress.2017.11.009
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    Cited by:

    1. Zheng Liu & Xin Liu & Hong-Zhong Huang & Pingyu Zhu & Zhongwei Liang, 2022. "A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle," Annals of Operations Research, Springer, vol. 311(1), pages 295-310, April.
    2. Chen, Rentong & Zhang, Chao & Wang, Shaoping & Zio, Enrico & Dui, Hongyan & Zhang, Yadong, 2023. "Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Hund, Lauren & Schroeder, Benjamin & Rumsey, Kellin & Huerta, Gabriel, 2018. "Distinguishing between model- and data-driven inferences for high reliability statistical predictions," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 201-210.
    4. Shi, Wen & Zhou, Qing & Zhou, Yanju, 2023. "An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Fang, Guanqi & Pan, Rong & Hong, Yili, 2020. "Copula-based reliability analysis of degrading systems with dependent failures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    6. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2021. "A single-loop Kriging coupled with subset simulation for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Wang, Fan & Li, Heng & Dong, Chao & Ding, Lieyun, 2019. "Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    8. Saberzadeh, Zahra & Razmkhah, Mostafa, 2022. "Reliability of degrading complex systems with two dependent components per element," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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