Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach |
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| Authors: | Refaul Ferdous Faisal Khan Rehan Sadiq Paul Amyotte Brian Veitch |
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| Institution: | 1. Faculty of Engineering & Applied Science, Memorial University, St. John''s, NL, Canada A1B 3X5;2. Okanagan School of Engineering, University of British Columbia, Kelowna, BC, Canada V1V 1V7;3. Department of Chemical Engineering & Applied Science, Dalhousie University, Halifax, NS, Canada B3J 2X4;1. Dipartimento di Ingegneria Chimica, Mineraria e delle Tecnologie Ambientali, Alma Mater Studiorum – Università di Bologna, via U. Terracini 28, 40131 Bologna, Italy;2. Institute for the Protection and Security of the Citizen, European Commission, Joint Research Centre, via E. Fermi 2749, 21027 Ispra (VA), Italy;1. Safety and Risk Engineering Group, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John?s, NL, Canada A1B 3X5;2. Offshore Safety and Risk Management Group, Australian Maritime College, University of Tasmania, Launceston, TAS 7250, Australia;3. SINTEF Technology and Society, Safety Research, 7465 Trondheim, Norway;1. SINTEF Technology and Society, Safety Research, Trondheim, Norway;2. Alma Mater Studiorum – Università di Bologna, Dipartimento di Ingegneria Chimica, Mineraria e delle Tecnologie Ambientali, Bologna, Italy;3. Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John''s, NL, Canada;4. Department of Process Engineering and Applied Science, Dalhousie University, Halifax, NS, Canada |
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| Abstract: | A bow-tie diagram combines a fault tree and an event tree to represent the risk control parameters on a common platform for mitigating an accident. Quantitative analysis of a bow-tie is still a major challenge since it follows the traditional assumptions of fault and event tree analyses. The assumptions consider the crisp probabilities and “independent” relationships for the input events. The crisp probabilities for the input events are often missing or hard to come by, which introduces data uncertainty. The assumption of “independence” introduces model uncertainty. Elicitation of expert's knowledge for the missing data may provide an alternative; however, such knowledge incorporates uncertainties and may undermine the credibility of risk analysis.This paper attempts to accommodate the expert's knowledge to overcome missing data and incorporate fuzzy set and evidence theory to assess the uncertainties. Further, dependency coefficient-based fuzzy and evidence theory approaches have been developed to address the model uncertainty for bow-tie analysis. In addition, a method of sensitivity analysis is proposed to predict the most contributing input events in the bow-tie analysis. To demonstrate the utility of the approaches in industrial application, a bow-tie diagram of the BP Texas City accident is developed and analyzed. |
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