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Correlation reliability assessment of artillery chassis transmission system based on CBN model

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  • Ding, Feng
  • Wang, Yihua
  • Ma, Guoliang
  • Zhang, Xinrui

Abstract

The traditional Bayesian Network (BN) has defects in the reliability assessment problem of multivariate correlation. In this paper, the Copula Bayesian Network (CBN) model is introduced into the artillery chassis transmission system for the first time to solve these problems. The model uses the Copula function to express the relationship between variables, and combines Bayesian Network probabilistic reasoning. On the basis of the traditional BN topology structure, the Copula function is introduced and transformed into CBN topology structure. The edge distribution of each node and the correlation coefficient matrix are used to construct the local connection function. It can realize the conversion of multi-variables into binary variables and construct high-dimensional functions. Bayesian network reasoning can get the failure probability of each node. Finally, the CBN model was used to assess the reliability of a certain type of artillery chassis transmission system for the first time. This method converts the multiple variables of the transmission system into a binary relationship, which is suitable for engineering applications and can express the correlation in the system. The computational result of this model is better than that of the traditional BN model.

Suggested Citation

  • Ding, Feng & Wang, Yihua & Ma, Guoliang & Zhang, Xinrui, 2021. "Correlation reliability assessment of artillery chassis transmission system based on CBN model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021004257
    DOI: 10.1016/j.ress.2021.107908
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    References listed on IDEAS

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    3. Pan, Yue & Ou, Shenwei & Zhang, Limao & Zhang, Wenjing & Wu, Xianguo & Li, Heng, 2019. "Modeling risks in dependent systems: A Copula-Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 416-431.
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    2. Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Zheng, Xiao-Wei & Li, Hong-Nan & Gardoni, Paolo, 2023. "Hybrid Bayesian-Copula-based risk assessment for tall buildings subject to wind loads considering various uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Qiao, Yajing & Wang, Shaoping & Shi, Jian & Liu, Di & Tao, Mo, 2024. "Reliability model based on fault energy dissipation for mechatronic system," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    5. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system based on multi-dimensional continuous-time Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

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