Algorithms for Bayesian network modeling and reliability inference of complex multistate systems with common cause failure
In constructing the Bayesian network (BN) reliability model, too many components will make the memory storage requirements of the conditional probability table (CPT) exceed the computer random access memory (RAM), especially for the complex multistate system with common cause failure (CCF). However,...
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| Vydáno v: | Reliability engineering & system safety Ročník 241; s. 109663 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.01.2024
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | In constructing the Bayesian network (BN) reliability model, too many components will make the memory storage requirements of the conditional probability table (CPT) exceed the computer random access memory (RAM), especially for the complex multistate system with common cause failure (CCF). However, the existing methods cannot solve the BN modeling’s large memory storage requirements problem of the complex multistate system with CCF. Thus, this paper proposes a BN block to process the nodes with CCF, converting CPT to a super multistate node’s joint probability table, based on which a multistate BN compression modeling algorithm under CCF is proposed to reduce the memory storage requirements of BN reliability modeling. By deriving the intermediate inference factor constructing rules, this paper proposes a multistate BN compression inference algorithm under CCF to perform the compressed BN reliability inference. Finally, two engineering cases validate the proposed algorithms’ performance. The results show that the proposed algorithms can significantly decrease the BN modeling’s memory storage requirements and accurately analyze the reliability of the complex multistate system with CCF.
•Proposing BN block to process CCF of complex multistate systems.•Deriving the constructing rules of intermediate inference factors with CCF.•Proposing the multistate BN compression inference algorithm under CCF. |
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| AbstractList | In constructing the Bayesian network (BN) reliability model, too many components will make the memory storage requirements of the conditional probability table (CPT) exceed the computer random access memory (RAM), especially for the complex multistate system with common cause failure (CCF). However, the existing methods cannot solve the BN modeling’s large memory storage requirements problem of the complex multistate system with CCF. Thus, this paper proposes a BN block to process the nodes with CCF, converting CPT to a super multistate node’s joint probability table, based on which a multistate BN compression modeling algorithm under CCF is proposed to reduce the memory storage requirements of BN reliability modeling. By deriving the intermediate inference factor constructing rules, this paper proposes a multistate BN compression inference algorithm under CCF to perform the compressed BN reliability inference. Finally, two engineering cases validate the proposed algorithms’ performance. The results show that the proposed algorithms can significantly decrease the BN modeling’s memory storage requirements and accurately analyze the reliability of the complex multistate system with CCF.
•Proposing BN block to process CCF of complex multistate systems.•Deriving the constructing rules of intermediate inference factors with CCF.•Proposing the multistate BN compression inference algorithm under CCF. |
| ArticleNumber | 109663 |
| Author | Zheng, Xiaohu Wang, Ning Xu, Yingchun Yao, Wen |
| Author_xml | – sequence: 1 givenname: Xiaohu orcidid: 0000-0003-4568-4277 surname: Zheng fullname: Zheng, Xiaohu email: zhengboy320@163.com organization: Defense Innovation Institute, Academy of Military Science, No. 53, Fengtai East Street, Beijing 100071, China – sequence: 2 givenname: Wen surname: Yao fullname: Yao, Wen email: wendy0782@126.com organization: Defense Innovation Institute, Academy of Military Science, No. 53, Fengtai East Street, Beijing 100071, China – sequence: 3 givenname: Yingchun surname: Xu fullname: Xu, Yingchun organization: Defense Innovation Institute, Academy of Military Science, No. 53, Fengtai East Street, Beijing 100071, China – sequence: 4 givenname: Ning orcidid: 0000-0002-8966-8909 surname: Wang fullname: Wang, Ning organization: Defense Innovation Institute, Academy of Military Science, No. 53, Fengtai East Street, Beijing 100071, China |
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| Keywords | Complex multistate system Reliability analysis Common cause failure Compression algorithm Bayesian network |
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failures publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2021.108028 |
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| StartPage | 109663 |
| SubjectTerms | Bayesian network Common cause failure Complex multistate system Compression algorithm Reliability analysis |
| Title | Algorithms for Bayesian network modeling and reliability inference of complex multistate systems with common cause failure |
| URI | https://dx.doi.org/10.1016/j.ress.2023.109663 |
| Volume | 241 |
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