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Reliability Risk Evaluation Method for Complex Mechanical System Based on Optimal Bayesian Network 被引量:3

Reliability Risk Evaluation Method for Complex Mechanical System Based on Optimal Bayesian Network
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摘要 In order to reduce the calculation of the failure probability in the complex mechanical system reliability risk evaluation,and to implement importance analysis of system components effectively,the system fault tree was converted into five different Bayesian network models. The Bayesian network with the minimum conditional probability table specification and the highest computation efficiency was selected as the optimal network. The two heuristics were used to optimize the Bayesian network. The fault diagnosis and causal reasoning of the system were implemented by using the selected Bayesian network. The calculation methods of Fussel-Vesely( FV),risk reduction worth( RRW),Birnbaum measure( BM) and risk achievement worth( RAW) importances were presented. A certain engine was taken as an application example to illustrate the proposed method. The results show that not only the correlation of the relevant variables in the system can be accurately expressed and the calculation complexity can be reduced,but also the relatively weak link in the system can be located accurately. In order to reduce the calculation of the failure probability in the complex mechanical system reliability risk evaluation,and to implement importance analysis of system components effectively,the system fault tree was converted into five different Bayesian network models. The Bayesian network with the minimum conditional probability table specification and the highest computation efficiency was selected as the optimal network. The two heuristics were used to optimize the Bayesian network. The fault diagnosis and causal reasoning of the system were implemented by using the selected Bayesian network. The calculation methods of Fussel-Vesely( FV),risk reduction worth( RRW),Birnbaum measure( BM) and risk achievement worth( RAW) importances were presented. A certain engine was taken as an application example to illustrate the proposed method. The results show that not only the correlation of the relevant variables in the system can be accurately expressed and the calculation complexity can be reduced,but also the relatively weak link in the system can be located accurately.
出处 《Journal of Donghua University(English Edition)》 EI CAS 2016年第2期177-182,共6页 东华大学学报(英文版)
基金 National Natural Science Foundations of China(Nos.61164009,61463021) the Science Foundation of Education Commission of Jiangxi Province,China(No.GJJ14420) the Young Scientists Object Program of Jiangxi Province,China(No.20144BCB23037) the Graduate Innovation Foundation of Jiangxi Province,China(No.YC2014-S364)
关键词 Bayesian network fault tree risk evaluation importance measure conditional probability table Bayesian network fault tree risk evaluation importance measure conditional probability table
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