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Fuzzy evidence theory and Bayesian networks for process systems risk analysis
Yazdi, M.
Yazdi, M.
Publication Date
2020
End of Embargo
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Rights
© 2018 Taylor & Francis. This is an Author's Original Manuscript of an article published by Taylor & Francis in Human and Ecological Risk Assessment: An International Journal on 25 Oct 2018 available online at http://www.tandfonline.com/10.1080/10807039.2018.1493679.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2018-06-24
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Additional title
Abstract
Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.
Version
Accepted manuscript
Citation
Yazdi M and Kabir S (2020) Fuzzy evidence theory and Bayesian networks for process systems risk analysis. Human and Ecological Risk Assessment: An International Journal. 26(1): 57-86.
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Type
Article