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Methodology for computer aided fuzzy fault tree analysis


Ferdous, R and Khan, F and Veitch, B and Amyotte, PR, Methodology for computer aided fuzzy fault tree analysis, Process Safety and Environmental Protection, 87, (4) pp. 217-226. ISSN 0957-5820 (2009) [Refereed Article]

DOI: doi:10.1016/j.psep.2009.04.004


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. © 2009.

Item Details

Item Type:Refereed Article
Keywords:Fault tree analysis; Fuzzy probability analysis; Fuzzy weighted index; Probabilistic risk assessment; Complex Processes; Computer aided; Cut sets; Design modifications; Failure data; Failure rate; Fault-tree; Fuzzy approach; Fuzzy weighted index
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Ocean engineering
Objective Division:Mineral Resources (Excl. Energy Resources)
Objective Group:Environmentally sustainable mineral resource activities
Objective Field:Environmentally sustainable mineral resource activities not elsewhere classified
UTAS Author:Khan, F (Professor Faisal Khan)
ID Code:94419
Year Published:2009
Web of Science® Times Cited:99
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2014-09-09
Last Modified:2015-01-27

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