System Risk Importance Analysis Using Bayesian Networks

Author(s):  
Ali Noroozian ◽  
Reza Baradaran Kazemzadeh ◽  
Seyed Taghi Akhavan Niaki ◽  
Enrico Zio

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.

2013 ◽  
Vol 23 (1) ◽  
pp. 399-411 ◽  
Author(s):  
Mark R. Blackburn ◽  
Art Pyster ◽  
Teresa Zigh ◽  
Richard Turner ◽  
Robin Dillon-Merrill

2017 ◽  
Vol 6 (2) ◽  
pp. 214-225
Author(s):  
Irene Podolak ◽  
Anteneh Ayanso ◽  
Maureen Connolly ◽  
Madelyn Law ◽  
Jarold Cosby

2014 ◽  
Vol 9 (1) ◽  
Author(s):  
François Champagne ◽  
Louise Lemieux-Charles ◽  
Marie-France Duranceau ◽  
Gail MacKean ◽  
Trish Reay

Author(s):  
Tolga Kurtoglu ◽  
Irem Y. Tumer

In this paper, we introduce a new risk-informed decision-making methodology for use during early design of complex systems. The proposed approach is based on the notion that a failure happens when a functional element in the system does not perform its intended task. Accordingly, risk is defined depending on the role of functionality in accomplishing designed tasks. A simulation-based failure analysis tool is used to analyze functional failures and their impact on overall system functionality. The analysis results are then integrated into a decision-making framework that relates the impact of functional failures and their propagation to decision making in order to guide system level design decisions. With the help of the proposed methodology, a multitude of failure scenarios can be quickly analyzed to determine the effects of decisions on overall system risk. Using this decision-making approach, design teams can systematically explore risks and vulnerabilities during early, functional stage of system development prior to the selection of specific components. Application of the presented method to a reservoir system design demonstrates these capabilities.


Author(s):  
Simon Candy

The use of Best Practical Environmental Optioneering (BPEO) has long been part of informed decision making within the Nuclear Industry. However, BPEO has typically been applied to specific and discrete objectives, for example the selection of a technology to treat a particular nuclear waste stream. While this has sometimes been extended to cover a number of objectives, no one had applied BPEO to a programme of the size and complexity of that associated with Legacy Ponds & Silos at Sellafield. The programme, spanning more than 30 years, includes a range of different objectives covering ongoing management, recovery, conditioning, storage and ultimately disposal of nuclear wastes. This range of activities is applied across a number of facilities containing multiple, significant waste streams. This paper explains how BPEO was applied to the Legacy Ponds & Silos programme and discussed some of the learning resulting from that exercise.


CIM Journal ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 274-281
Author(s):  
D. Beneteau ◽  
K. Chovan ◽  
P. Hughes ◽  
S. Gauthier

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