Methodology for computer aided fuzzy fault tree analysis |
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| Authors: | Refaul Ferdous Faisal Khan Brian Veitch Paul R Amyotte |
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| Institution: | 1. Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John''s, Newfoundland, Canada A1B 3X5;2. Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada B3J 2X4;1. Civil and Environmental Engineering Department, Amirkabir University of Technology, Hafez St., Tehran, Iran;2. School of Computing and Engineering, University of West London, St Mary’s Rd, London, W5 5RF, UK;1. Univ Lille Nord de France, IFSTTAR, COSYS, ESTAS, Villeneuve d?Ascq, France;2. Univ Lille Nord de France, IFSTTAR, COSYS, LEOST, Villeneuve d?Ascq, France;1. Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No. 66, Changjiang West Road, Qingdao, China;2. Mechanical and Electronic Engineering, China University of Petroleum (East China), No. 66, Changjiang West Road, Qingdao, China;1. Department of Mechanical Engineering, Ramco Institute of Technology, Rajapalayam 626117, India;2. Dr. Sivanthi Aditanar College of Engineering, Tiruchendur 628215, India;3. Department of Mechanical Engineering, Kalasalingam University, Anand Nagar, Krishnankoil 626126, India |
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| Abstract: | Probabilistic risk assessment (PRA) is a comprehensive, structured and logical analysis method aimed at identifying and assessing risks of complex process systems. PRA uses fault tree analysis (FTA) as a tool to identify basic causes leading to an undesired event, to represent logical dependency of these basic causes in leading to the event, and finally to calculate the probability of occurrence of this event.To conduct a quantitative fault tree analysis, one needs a fault tree along with failure data of the basic events (components). Sometimes it is difficult to have an exact estimation of the failure rate of individual components or the probability of occurrence of undesired events due to a lack of sufficient data. Further, due to imprecision in basic failure data, the overall result may be questionable. To avoid such conditions, a fuzzy approach may be used with the FTA technique. This reduces the ambiguity and imprecision arising out of subjectivity of the data.This paper presents a methodology for a fuzzy based computer-aided fault tree analysis tool. The methodology is developed using a systematic approach of fault tree development, minimal cut sets determination and probability analysis. Further, it uses static and dynamic structuring and modeling, fuzzy based probability analysis and sensitivity analysis.This paper also illustrates with a case study the use of a fuzzy weighted index and cutsets importance measure in sensitivity analysis (for system probabilistic risk analysis) and design modification. |
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