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System Risk Importance Analysis Using Bayesian Networks

Department of Industrial and Systems Engineering, Tarbiat Modares University, Jalal AleAhmad Highway, Tehran, 9821, Iran

E-mail Address: a.noroozian@modares.ac.ir

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Reza Baradaran Kazemzadeh

Department of Industrial and Systems Engineering, Tarbiat Modares University, Jalal AleAhmad Highway, Tehran, 9821, Iran

E-mail Address: rkazem@modares.ac.ir

Corresponding author.

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Seyed Taghi Akhavan Niaki

Department of Industrial Engineering, Sharif University of Technology, Azani Avenue, Tehran 1458889694, Iran

E-mail Address: Niaki@sharif.edu

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, and

Enrico Zio

Chaire Systems Science and the Energy Challenge, Fondation Electricite’ de France (EDF), Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay Grande voie des Vignes, 92290 Chatenay-Malabry, France

E-mail Address: enrico.zio@polimi.it

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https://doi.org/10.1142/S0218539318500043Cited by:10 (Source: Crossref)

Abstract

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.

Keywords:

References

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Vol. 25, No. 01

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History

Received 3 October 2016

Revised 3 June 2017

Accepted 4 August 2017

Published: 13 September 2017

Keywords

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System Risk Importance Analysis Using Bayesian Networks

What is it about?

Importance measures (IMs) are used for risk-informed decision making in system operations, safety, and maintenance. Traditionally, they are computed within fault tree analysis. Although fault tree 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.

Why is it important?

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.

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System Risk Importance Analysis Using Bayesian Networks

Abstract

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