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    Fuzzy evidence theory and Bayesian networks for process systems risk analysis

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    Publication date
    2020
    Author
    Yazdi, M.
    Kabir, Sohag
    Keyword
    Risk analysis
    Fault tree analysis
    Process safety
    Evidence theory
    Fuzzy set theory
    Bayesian networks
    Uncertainty analysis
    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
    
    Metadata
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    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.
    URI
    http://hdl.handle.net/10454/17431
    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.
    Link to publisher’s version
    https://doi.org/10.1080/10807039.2018.1493679
    Type
    Article
    Collections
    Engineering and Informatics Publications

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