The application of Fuzzy logic to determine the failure probability in Fault Tree Risk Analysis
Irtiqa Imini Pishgiri Masdumiyat (Safety Promotion and Injury Prevention),
Vol. 2 No. 2 (2014),
20 December 2014
,
Page 113-123
https://doi.org/10.22037/meipm.v2i2.7052
Abstract
Background and objective: FTA is an appropriate tool for failure analysis and determining the failure rate. Sometimes, the lack of information on the process and main events, it is difficult to accurately determine the probability rate of the failures. The application of Fuzzy tool can be help-ful in such cases. In present study the Fuzzy logic was applied to determine the failures probability of a distillation unit in an oil refinery in its risk assessment using FTA. The method and the results of the reactor risk analysis are discussed in present paper.
Materials and method:In this descriptive study, HAZOP was applied to determine the process failures. Its fault tree was determined then. The probability rate of those failures which had the required input data for fault tree were determined using their equations. Otherwise, Fuzzy logic was used. The Fuzzy process began with the selection of an expertise team and ended with deter-mination of the probability rate for the route events. The equation recommended by Onisawa was used to determine the probability rate. Then the probability of top events was determined using the appropriate equation. The critical Minimal Cut Sets (MCS) were determined using Fussell-Vesely equation. In the end, the influence of deleting three critical MCSs alone and simultaneously was studied.
Results:The application of HAZOP led to determine 4 main and 13 end failures. From13 end failures,7 failures were Fuzzy and 6 were probabilistic. The results revealed that cutting of the most critical MCS (e.g. A.3.2) may increase the reliability of top event by 5.9% from 0.914 to 0.968 .Also cutting of all three main critical MCSs (e.g. A.2.2 A.3.1 A.3.2) may increase the reliability of top event by 8.4% from 0.914 to 0.991.
Conclusion:The failure probability can be determined using Fuzzy logic when there is not enough data on process and top events.
How to cite this article: Mirza S, Jafari MJ, Omidvari M, Miri Lavasani SMR.The application of Fuzzy logic to determine the failure probability in Fault Tree Risk Analysis. Irtiqa Imini Pishgiri Masdumiyat (Safety Promotion and Injury Prevention).2014;2(2):113-123.
- Fuzzy logic, failure rate, Fault tree
How to Cite
References
Clemen RT, Winkler RL. Combining probability distributions from experts in risk analysis. Risk analysis. 1999;19(2):187-203.
Amrozowicz MD, Brown A, Golay M. A probabilistic analysis of tanker groundings. 7th international offshore and polar engineering conference; Honolulu, Hawaii;1997. 1-19.
Zadeh LA. Fuzzy sets. Information and control. 1965;8(3):338-53.
Zimmermann HJ. Fuzzy Set Theory and its Applications. 2nd ed. Boston: Kluwer Academic Publisher; 1991. 36-43.
Tyagi SK, Pandey D, Tyagi R. Fuzzy set theoretic approach to fault tree analysis. International Journal of Engineering, Science and Technology. 2010;2(5):276-83.
Vose D. Risk analysis: a quantitative guide. 2nd ed. New York: John Wiley & Sons; 2000.
Andrews JD, Ridley LM. Application of the cause–consequence diagram method to static systems. Reliability Engineering & System Safety. 2002;75(1):47-58.
Renjith V, Madhu G, Nayagam V, Bhasi A. Two-dimensional fuzzy fault tree analysis for chlorine release from a chlor-alkali industry using expert elicitation. Journal of hazardous materials. 2010;183(1):103-10.
Onisawa T. An application of fuzzy concepts to modelling of reliability analysis. Fuzzy sets and Systems. 1990;37(3):267-86.
Onisawa T. Subjective analysis of system reliability and its analyzer. Fuzzy Sets and Systems. 1996;83(2):249-69.
Zhao R, Govind R. Defuzzification of fuzzy intervals. Fuzzy Sets and Systems. 1991;43(1):45-55.
Miller GA. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological review. 1956;63(2):81-97.
Cornelissen A, Van Den Berg J, Koops W, Kaymak U. Elicitation of expert knowledge for fuzzy evaluation of agricultural production systems. Agriculture, ecosystems & environment. 2003;95(1):1-18.
Nurmi H. Approaches to collective decision making with fuzzy preference relations. Fuzzy sets and systems. 1981;6(3):249-59.
Ishikawa A, Amagasa M, Shiga T, Tomizawa G, Tatsuta R, Mieno H. The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy sets and systems. 1993;55(3):241-53.
Miri Lavasani S, Yang Z, Finlay J, Wang J. Fuzzy risk assessment of oil and gas offshore wells. Process Safety and Environmental Protection. 2011;89(5):277-94.
Chen SJ, Hwang CL, Hwang FP. Fuzzy Multiple Attribute Decision Making: Methods and Applications. 1st ed. New York: Springer-Verlag; 1992.
Onisawa T. An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets and Systems. 1988;27(2):87-103.
Hauge S, Onshus T. Reliability Data for Safety Instrumented Systems: PDS Data Handbook. 1st ed. New York: SINTEF Technology and Society; 2010.
Pinto A, Nunes IL, Ribeiro RA. Qualitative model for risk assessment in construction industry: a fuzzy logic approach. Emerging Trends in Technological Innovation: Springer; 2010. p. 105-11.
Celik M, Lavasani SM, Wang J. A risk-based modelling approach to enhance shipping accident investigation. Safety Science. 2010;48(1):18-27.
- Abstract Viewed: 2587 times
- PDF Downloaded: 2253 times