An imprecise Fault Tree Analysis for the estimation of the Rate of OCcurrence Of Failure (ROCOF)

Journal of Loss Prevention in the Process Industries

Volumn 26, Issue 6, 2013, Pages 1285-1292

  Curcurù, Giuseppe ^a   Galante, Giacomo Maria ^a   La Fata, Concetta Manuela ^a  

^a UNIVERSITY OF PALERMO   (Italy)

Author keywords

DempstereShafer Theory; Enabler events; Fault Tree Analysis; Initiator Events; Rate of Occurrence of Failure

Indexed keywords

ACCIDENT PREVENTION; INDUSTRIAL PLANTS; RELIABILITY ANALYSIS; RISK PERCEPTION; TREES (MATHEMATICS);

CLASSICAL PROBABILITIES; DEMPSTERESHAFER THEORY; ENABLER EVENTS; INDUSTRIAL SCENARIOS; INDUSTRIAL SYSTEMS; MATHEMATICAL FRAMEWORKS; NORMAL OPERATING CONDITIONS; PROCESS PARAMETERS;

FAULT TREE ANALYSIS;

FORESTRY; RISK ASSESSMENT; SAFETY;

EID: 84901696698     PISSN: 09504230     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jlp.2013.07.006     Document Type: Article

References (29)

1. American Institute of Chemical Engineers (AIChe). (2000). Guidelines for chemical process quantitative risk analysis (2nd ed.). New York: Center for Chemical Process Safety of the AIChE.
2. Bae, H. R., Grandhi, R. V., & Canfield, R. A. (2003). Uncertainty quantification of structural response using evidence theory. American Institute of Aeronautics and Astronautics Journal, 41(10), 2062-2068.
3. Bae, H. R., Grandhi, R. V., & Canfield, R. A. (2004). An approximation approach for uncertainty quantification using evidence theory. Reliability Engineering and System Safety, 86, 215-225.
4. Curcurù, G., Galante, G. M., & La Fata, C. M. (2012a). Epistemic uncertainty in fault tree analysis approached by the evidence theory. Journal of Loss Prevention in the Process Industries, 25, 667-676.
5. Curcurù, G., Galante, G. M., & La Fata, C. M. (2012b). A bottom-up procedure to calculate the top event probability in presence of epistemic uncertainty. In Proceedings of the PSAM11 & ESREL 2012, Helsinki, Finland.
6. Dubois, D., & Prade, H. (1986). On the unicity of Dempster rule of combination. International Journal of Intelligent System, 1, 133-142.
7. Ferdous, R., Khan, F., Sadiq, R., & Amyotte, P. (2009). Methodology for computer aided fuzzy fault tree analysis. Process Safety and Environmental Protection, 87, 217-226.
8. Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., & Veitch, B. (2012). Handling and updating uncertain information in bow-tie analysis. Journal of Loss Prevention in the Process Industries, 25, 8-19.
9. Ferson, S., Cooper, J. A., & Myers, D. (2000). Beyond point estimates: risk assessment using interval. fuzzy and probabilistic arithmetic. In Workshop notes at society of risk analysis annual meeting.
10. Ferson, S., & Ginzburg, L. R. (1996). Different methods are needed to propagate ignorance and variability. Reliability Engineering and System Safety, 54, 133-144.
11. Guth, M. A. S. (1991). A probabilistic foundation for vagueness & imprecision in fault-tree analysis. IEEE Transactions On Reliability, 40(5), 563-571.
12. Helton, J. C., Johnson, J. D., & Oberkampf, W. L. (2004). An exploration of alternative approaches to the representation of uncertainty in model predictions. Reliability Engineering and System Safety, 85, 39-71.
13. Hoffman, F. O., & Hammonds, J. S. (1994). Propagation of uncertainty in risk analysis assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Analysis, 14(5), 707-712.
14. IEC 61025. (2006). Fault tree analysis (2nd ed.).
15. IEC 61508. (1999). Functional safety of electrical/electronic/programmable electronic safety-related systems. Geneva: IEC (International Electrotechnical Commission).
16. IEC 61511. (2003). Functional safety-Safety instrumented systems for the process industry sector. Geneva: IEC (International Electrotechnical Commission).
17. ISO/IEC Guide 51 IEC 60300-3-9. (1999). Risk analysis of technological systems.
18. Klawonn, F., & Schwecke, E. (1992). On the axiomatic justification of Dempster's rule combination. International Journal of Intelligent Systems, 7, 469-478.
19. Markowski, A. S., Mannan, M. S., & Bigoszewska, A. (2009). Fuzzy logic for process safety analysis. Journal of Loss Prevention in the Process Industries, 22, 695-702.
20. Modarres, M., Kaminsky, M., & Krivtsov, V. (2010). Reliability engineering and risk analysis: A practical guide (2nd ed.). Taylor and Francis Group: CRC Press.
21. Phillips, M. J. (2001). Estimation of the expected ROCOF of a repairable system with bootstrap confidence region. Quality and Reliability Engineering International, 17, 159-162.
22. Rausand, M., & Høyland, A. (2004). System reliability theory: Models, statistical methods, and applications (2nd ed.). New Jersey: John Wiley & Sons, Inc.
23. Sentz, K., & Ferson, S. (2002). Combination of evidence in Dempstere Shafer theory. Technical Report SAND 2002-0835. Albuquerque, New Mexico: Sandia National Laboratories.
24. Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton University Press.
25. Tan, F. R., Jiang, Z. B., & Bai, T. S. (2008). Reliability analysis of repairable systems using stochastic point processes. Journal of Shanghai Jiaotong University (Science), 13(3), 366-369.
26. Voorbraak, F. (1991). On the justification of Dempster's rule of combinations. Artificial Intelligence, 48, 171-197.
27. Yager, R. R. (1987). On the Dempstere Shafer framework and new combination rules. Info Sciences, 41, 93-137.
28. Zimmermann, H. J. (1991). Fuzzy set theory and its application (2nd ed.). Kluwer Academic Publishers.
29. Zio, E. (2007). An introduction to the basics of reliability and risk analysis (series on quality, reliability and engineering statistics). Singapore: World Scientific Publishing Co. Pte. Ltd.