System Risk Importance Analysis Using Bayesian Networks

被引：6

作者：

Noroozian, Ali ^{[1
]}

Kazemzadeh, Reza Baradaran ^{[1
]}

Niaki, Seyed Taghi Akhavan ^{[2
]}

Zio, Enrico ^{[3
]}

机构：

[1] Tarbiat Modares Univ, Dept Ind & Syst Engn, Jalal AleAhmad Highway, Tehran 9821, Iran

[2] Sharif Univ Technol, Dept Ind Engn, Azani Ave, Tehran 1458889694, Iran

[3] Univ Paris Saclay Grande Voie des Vignes, Lab Genie Ind, Cent Supelec, Chaire Syst Sci & Energy Challenge,Fdn Elect Fran, F-92290 Chatenay Malabry, France

来源：

INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING | 2018年 / 25卷 / 01期

关键词：

System risk; importance measures; Bayesian networks; fault tree analysis;

D O I：

10.1142/S0218539318500043

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Importance measures (IMs) are used for risk-informed decision making in system operations, safety, and maintenance. Traditionally, they are computed within fault tree (FT) analysis. Although FT analysis is a powerful tool to study the reliability and structural characteristics of systems, Bayesian networks (BNs) have shown explicit advantages in modeling and analytical capabilities. In this paper, the traditional definitions of IMs are extended to BNs in order to have more capability in terms of system risk modeling and analysis. Implementation results on a case study illustrate the capability of finding the most important components in a system.

引用

页数：26

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