Journal of Failure Analysis and Prevention

, Volume 17, Issue 4, pp 756–764 | Cite as

Hybrid Probabilistic Risk Assessment Using Fuzzy FTA and Fuzzy AHP in a Process Industry

Technical Article---Peer-Reviewed

Abstract

There are many available techniques which are widely used for failure probability analysis. Fault tree analysis (FTA) is a well-known method to identify the basic events (BEs) to reach top event. However, the FTA method in real circumstances is limited because of the many unknown and the vagueness of the situations. Thus, fuzzy set theory with respect to subjective expert opinion is employed to cope with the uncertain knowledge of BEs including randomness, ignorance, and shortages of data. In addition, to gain this purpose, much subjectivity may appear; as an example, the main one is the expert weighting. This study highlights the utility of fuzzy set theory and analytic hierarchy process to failure probability analysis in a case study. A chemical process plant has been selected to illustrate the application of proposed model with a comparison of the results with conventional model.

Keywords

Fuzzy AHP Failure analysis Fuzzy set theory Fault tree 

Notes

Acknowledgments

The author sincerely thank the editor and the anonymous reviewers for their insights and helpful comments and suggestions which are very helpful in improving the quality of the paper.

References

  1. 1.
    H. Acosta, B.M. Forrest, The spread of marine non-indigenous species via recreational boating: a conceptual model for risk assessment based on fault tree analysis. Ecol. Modell. 220, 1586–1598 (2009). doi: 10.1016/j.ecolmodel.2009.03.026 CrossRefGoogle Scholar
  2. 2.
    K. Alkhaledi, S. Alrushaid, J. Almansouri, A. Alrashed, Using fault tree analysis in the Al-Ahmadi town gas leak incidents. Saf. Sci. 79, 184–192 (2015). doi: 10.1016/j.ssci.2015.05.015 CrossRefGoogle Scholar
  3. 3.
    J.J. Buckley, Fuzzy hierarchical analysis. Fuzzy Sets Syst. 17, 233–247 (1985). doi: 10.1016/0165-0114(85)90090-9 CrossRefGoogle Scholar
  4. 4.
    H.K. Chan, X. Wang, Fuzzy extent analysis for food risk assessment, Fuzzy Hierarchical Model for Risk Assessment (Springer, London, 2013), pp. 89–114. doi: 10.1007/978-1-4471-5043-5_6 CrossRefGoogle Scholar
  5. 5.
    D.-Y. Chang, Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95, 649–655 (1996). doi: 10.1016/0377-2217(95)00300-2 CrossRefGoogle Scholar
  6. 6.
    C.-Y. Cheng, S.-F. Li, S.-J. Chu, C.-Y. Yeh, R.J. Simmons, Application of fault tree analysis to assess inventory risk: a practical case from aerospace manufacturing. Int. J. Prod. Res. 51, 6499–6514 (2013). doi: 10.1080/00207543.2013.825744 CrossRefGoogle Scholar
  7. 7.
    M. Gul, A.F. Guneri, A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. J. Loss Prev. Process Ind. 40, 89–100 (2016). doi: 10.1016/j.jlp.2015.11.023 CrossRefGoogle Scholar
  8. 8.
    H.-M. Hsu, C.-T. Chen, Aggregation of fuzzy opinions under group decision making. Fuzzy Sets Syst. 79, 279–285 (1996). doi: 10.1016/0165-0114(95)00185-9 CrossRefGoogle Scholar
  9. 9.
    C.-L. Hwang, K. Yoon, Multiple Attribute Decision Making (Springer, New York, 1981). doi: 10.1007/978-3-642-48318-9 CrossRefGoogle Scholar
  10. 10.
    A. Kaufmann, M.M. Gupta, Introduction to Fuzzy Arithmetic: theory and Applications (Van Nostrand Reinhold Co., New York, 1985)Google Scholar
  11. 11.
    S.M. Lavasani, N. Ramzali, F. Sabzalipour, E. Akyuz, Utilisation of fuzzy fault tree analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural-gas wells. Ocean Eng. 108, 729–737 (2015). doi: 10.1016/j.oceaneng.2015.09.008 CrossRefGoogle Scholar
  12. 12.
    S.M. Lavasani, A. Zendegani, M. Celik, An extension to Fuzzy Fault Tree Analysis (FFTA) application in petrochemical process industry. Process Saf. Environ. Prot. 93, 75–88 (2015). doi: 10.1016/j.psep.2014.05.001 CrossRefGoogle Scholar
  13. 13.
    A. Mentes, I.H. Helvacioglu, An application of fuzzy fault tree analysis for spread mooring systems. Ocean Eng. 38, 285–294 (2011). doi: 10.1016/j.oceaneng.2010.11.003 CrossRefGoogle Scholar
  14. 14.
    M.R. Miri Lavasani, J. Wang, Z. Yang, J. Finlay, Application of fuzzy fault tree analysis on oil and gas offshore pipelines. Int. J. Mar. Sci. Eng 1, 29–42 (2011)Google Scholar
  15. 15.
    M. Nadjafi, M.A. Farsi, H. Jabbari, Reliability analysis of multi-state emergency detection system using simulation approach based on fuzzy failure rate. Int. J. Syst. Assur. Eng. Manag. (2016). doi: 10.1007/s13198-016-0563-7 Google Scholar
  16. 16.
    M. Omidvari, S.M.R. Lavasani, S. Mirza, Presenting of failure probability assessment pattern by FTA in Fuzzy logic (case study: distillation tower unit of oil refinery process). J. Chem. Heal. Saf. 21, 14–22 (2014). doi: 10.1016/j.jchas.2014.06.003 CrossRefGoogle Scholar
  17. 17.
    T. Onisawa, Subjective analysis of system reliability and its analyzer. Fuzzy Sets Syst. 83, 249–269 (1996). doi: 10.1016/0165-0114(95)00381-9 CrossRefGoogle Scholar
  18. 18.
    T. Onisawa, An application of fuzzy concepts to modelling of reliability analysis. Fuzzy Sets Syst. 37, 267–286 (1990). doi: 10.1016/0165-0114(90)90026-3 CrossRefGoogle Scholar
  19. 19.
    T. Onisawa, A representation of human reliability using fuzzy concepts. Inf. Sci. (Ny) 45, 153–173 (1988). doi: 10.1016/0020-0255(88)90038-2 CrossRefGoogle Scholar
  20. 20.
    T. Onisawa, An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets Syst. 27, 87–103 (1988). doi: 10.1016/0165-0114(88)90140-6 CrossRefGoogle Scholar
  21. 21.
    T. Onisawa, K.B. Misra, Use of fuzzy sets theory: (part-ii: applications) Chap. 14, in Fundamental Studies in Engineering, (1993), pp. 551–586. doi: 10.1016/B978-0-444-81660-3.50024-1
  22. 22.
    N. Ramzali, M.R.M. Lavasani, J. Ghodousi, Safety barriers analysis of offshore drilling system by employing Fuzzy event tree analysis. Saf. Sci. 78, 49–59 (2015). doi: 10.1016/j.ssci.2015.04.004 CrossRefGoogle Scholar
  23. 23.
    E. Ruijters, M. Stoelinga, Fault tree analysis: a survey of the state-of-the-art in modeling, analysis and tools. Comput. Sci. Rev. 15, 29–62 (2015). doi: 10.1016/j.cosrev.2015.03.001 CrossRefGoogle Scholar
  24. 24.
    T. Runkler, M. Glesner, A set of axioms for defuzzification strategies towards a theory of rational defuzzification operators. Proc. Second IEEE Int. Conf. Fuzzy Set Syst. pp. 1161–1166 (1993)Google Scholar
  25. 25.
    M. Sugeno, H.T. Nguyen, N.R. Prasad, Fuzzy Modeling and Control: Selected Works of M. Sugeno (CRC Press, Baco Raton, 1999)Google Scholar
  26. 26.
    D. Wang, P. Zhang, L. Chen, Fuzzy fault tree analysis for fire and explosion of crude oil tanks. J. Loss Prev. Process Ind. 26, 1390–1398 (2013). doi: 10.1016/j.jlp.2013.08.022 CrossRefGoogle Scholar
  27. 27.
    J. Wang, F. Wang, S. Chen, J. Wang, L. Hu, Y. Yin, Y. Wu, Fault-tree-based instantaneous risk computing core in nuclear power plant risk monitor. Ann. Nucl. Energy 95, 35–41 (2016). doi: 10.1016/j.anucene.2016.02.024 CrossRefGoogle Scholar
  28. 28.
    Z.M. Xue, Research on FTA of fire and explosion in the crude oil gathering-transport combination station. Procedia Eng. 11, 575–582 (2011). doi: 10.1016/j.proeng.2011.04.698 CrossRefGoogle Scholar
  29. 29.
    M. Yazdi, The application of bow-tie method in hydrogen sulfide risk management using layer of protection analysis (LOPA). J. Fail. Anal. Prev. 17, 291–303 (2017). doi: 10.1007/s11668-017-0247-x CrossRefGoogle Scholar
  30. 30.
    M. Yazdi, S. Daneshvar, H. Setareh, An extension to Fuzzy Developed Failure Mode and Effects Analysis (FDFMEA) application for aircraft landing system. Saf. Sci. 98, 113–123 (2017). doi: 10.1016/j.ssci.2017.06.009 CrossRefGoogle Scholar
  31. 31.
    M. Yazdi, F. Nikfar, M. Nasrabadi, Failure probability analysis by employing fuzzy fault tree analysis. Int. J. Syst. Assur. Eng. Manag. (2017). doi: 10.1007/s13198-017-0583-y Google Scholar
  32. 32.
    L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965). doi: 10.1016/S0019-9958(65)90241-X Google Scholar
  33. 33.
    R. Zhao, R. Govind, Defuzzification of fuzzy intervals. Fuzzy Sets Syst. 43, 45–55 (1991). doi: 10.1016/0165-0114(91)90020-Q CrossRefGoogle Scholar

Copyright information

© ASM International 2017

Authors and Affiliations

  1. 1.Department of Industrial EngineeringEastern Mediterranean UniversityFamagustaTurkey

Personalised recommendations