Document Type : Research Paper


Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Iran.


One of the most important issues that organizations have to deal with is the timely identification and detection of risk factors aimed at preventing incidents. Managers’ and engineers’ tendency towards minimizing risk factors in a service, process or design system has obliged them to analyze the reliability of such systems in order to minimize the risks and identify the probable errors. Concerning what was just mentioned, a more accurate Failure Mode and Effects Analysis (FMEA) is adopted based on fuzzy logic and fuzzy numbers. Fuzzy  TOPSIS is also used to identify, rank, and prioritize error and risk factors. This paper uses FMEA as a risk identification tool. Then, Fuzzy Risk Priority Number (FRPN) is calculated and triangular fuzzy numbers are prioritized through Fuzzy TOPSIS. In order to have a better understanding toward the mentioned concepts, a case study is presented.


Main Subjects

[1]       Besterfield, D. H. (2001). Quality control. 6th editionPrentice Hall.
[2]       Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability engineering and system safety50(2), 203-213.
[3]       Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In lecture notes in economics and mathematical systems (pp. 289-486). Springer.
[4]       Chang, C. L., Wei, C. C., & Lee, Y. H. (1999). Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes, 28(9), 1072-1080.
[5]       Chrysler, L. L. C. (2008). Ford motor company, general motors corporation. Potential Failure Mode and Effect Analysis (FMEA), 67-112.
[6]       Dale, B. G., & Shaw, P. (1990). Failure mode and effects analysis in the UK motor industry: A state‐of‐the‐art study. Quality and reliability engineering international6(3), 179-188.
[7]       Gharachorlu, N. (2006). Risk assessment and management. Tehran Science and Techniques Publications.
[8]       Ghazanfari. M., & Rezaei, M. (2005). An introduction to fuzzy sets theory. Iran University of Science and Technology. (In Persian).
[9]       Guimaraes, A. C., & Lapa, C. M. F. (2004). Effect’s analysis fuzzy inference system in nuclear problems using approximate reasoning. Annals of nuclear energy31(1), 107-115.
[10]    Guimaraes, A. C. F., & Lapa, C. M. F. (2007). Fuzzy inference to risk assessment on nuclear engineering systems. Applied soft computing7(1), 17-28.
[11]    Ilangkumaran, M., & Thamizhselvan, P. (2010). Integrated hazard and operability study using fuzzy linguistics approach in petrochemical industry. International journal of quality & reliability management, 27(5), 541-557.
[12]    Ireson, G., Coombs, W., Clyde, F., & Richard, Y. M. (1995). Handbook of reliability engineering and management. McGraw-Hill Professional.
[13]    Bowles, J. B., & Pelaez, C. E. (1996). Using fuzzy cognitive maps as a system model for failure modes and effect analysis. Information science88(1-4), 177-199.
[14]    Lee, H. M. (1996). Applying fuzzy set theory to evaluate the rate of aggregative risk in software development. Fuzzy sets and systems79(3), 323-336.
[15]    Tay, K. M., & Lim, C. P. (2006). Fuzzy FMEA with a guided rules reduction system for prioritization of failures. International journal of quality & reliability management, 23(8), 1047-1066.
[16]    Lavasani, S. M., Yang, Z., Finlay, J., & Wang, J. (2011). Fuzzy risk assessment of oil and gas offshore wells. Process safety and environmental protection89(5), 277-294.    
[17]    Mohr, R.R., (2002). Failure modes and effect analysis. JACOBS SVERDRUP (JE Publications).
[18]    Pillay, A., & Wang, J. (2003). Modified failure mode and effects analysis using approximate reasoning. Reliability engineering & system safety79(1), 69-85.
[19]    Russomanno, D. J., Bonnell, R. D., & Bowles, J. B. (1992, January). A blackboard model of an expert system for failure mode and effects analysis. Annual reliability and maintainability symposium 1992 proceedings (pp. 483-490). IEEE.DOI: 10.1109/ARMS.1992.187869
[20]         Sadiq, R., & Husain, T. (2005). A fuzzy-based methodology for an aggregative environmental risk assessment: a case study of drilling waste. Environmental modelling & software20(1), 33-46.
[21]         Sankar, N. R., & Prabhu, B. S. (2001). Modified approach for prioritization of failures in a system failure mode and effects analysis. International Journal of quality & reliability management. 18(3), 324-336.
[22]          Shirouyehzad, H., Khodadadi-Karimvand, M., & Dabestani, R. (2009). Prioritizing the factors causing hazard; using Fuzzy FMEA. 2th International conference of industrial safety, occupational health and environments in organization. Tehran, Iran.
[23]         Teng, S. H. G., & Ho, S. Y. M. (1996). Failure mode and effects analysis. International journal of quality and reliability management, 13(5), 8-26.
[24]         Wang, Y. M., & Elhag, T. M. (2007). A fuzzy group decision making approach for bridge risk assessment. Computers and industrial engineering53(1), 137-148.
[25]         Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications36(2), 1195-1207.
[26]         Xu, K., Tang, L. C., Xie, M., Ho, S. L., & Zhu, M. L. (2002). Fuzzy assessment of FMEA for engine systems. Reliability engineering & system safety75(1), 17-29.