In this paper, we study the applicability of the monotone output property and the output resolution property in fuzzy assessment models to two industrial Failure Mode and Effect Analysis (FMEA) problems. First, the effectiveness of the monotone output property in a single-input fuzzy assessment model is demonstrated with a proposed fuzzy occurrence model. Then, the usefulness of the two properties to a multi-input fuzzy assessment model, i.e., the Bowles fuzzy Risk Priority Number (RPN) model, is assessed. The experimental results indicate that both the fuzzy occurrence model and Bowles fuzzy RPN model are able to fulfill the monotone output property, with the derived conditions (in Part I) satisfied. In addition, the proposed rule refinement technique is able to improve the output resolution property of the Bowles fuzzy RPN model.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Bell, D., Cox, L., Jackson, S., & Schaefer, P. (1992). Using causal reasoning for automated failure modes and effects analysis (FMEA). Annual reliability and maintainability symposium, pp. 343–353.
Ben-Daya M. and Raouf A. (1993). A revised failure mode and effects analysis model. International Journal of Quality & Reliability Management 3: 43–47
Bowles, J. B. (1998). The new SAE FMECA standard. In Proceedings of Annual Reliability and Maintainability Symposium, pp. 48–53.
Bowles J.B. and Peláez C.E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering & System Safety 50: 203–213
Chatterji, M. (2003). Designing and using tools for educational assessment. Pearson Education.
Chrysler Corporation, Ford Motor Company and General Motors Corporation. (1995). Potential failure mode and effect analysis (FMEA) reference manual (SAE J 1739). AIAG.
Cunningham, G. K. (1986). Educational and psychological measurement. Macmillan publishing.
Dubois, D., & Prade, H. (1997). Fuzzy criteria and fuzzy rules in subjective evaluation—a general discussion. In Proceedings of 5th European Congress on Intelligent Technologies and Soft Computing, pp. 975–978.
Figueira, J., Greco, S., & Ehrgott, M. (2005). Multiple criteria decision analysis: State of art surveys. Springer International Series.
Guimarães A.C.F. and Lapa C.M.F. (2004a). Effects analysis fuzzy inference system in nuclear problems using approximate reasoning. Annals of Nuclear Energy 31: 107–115
Guimarães A.C.F. and Lapa C.M.F. (2004b). Fuzzy FMEA applied to PWR chemical and volume control system. Progress in Nuclear Energy 44: 191–213
Hisao, I. (1991). Iterative fuzzy modeling and a hierarchical network. In Proceedings of 4th International Fuzzy System Association World Congress, pp. 49–52.
Hisao I., Tomoharu N., Takashi and Y. (2006). An approach to fuzzy default reasoning for function approximation. Soft Computing 10: 850–864
Ireson, W. G., Coombs, C. F., & Moss, R. Y. (1995). Handbook of reliability engineering and management. McGraw-Hill Professional.
Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neural-fuzzy and soft computing. Prentice-Hall.
Kaliszewski, I. (2006). Soft computing for multiple criteria decision making. Springer.
Lee, B. H. (2001). Using Bayes belief networks in industrial FMEA modeling and analysis. Annual reliability and maintainability symposium, pp. 7–15.
Lin, C. T., & Lee, C. S. G. (1995). Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Prentice-Hall.
Masters, T. (1993). Practical neural network recipes in C++. Academic Press.
Masters, T. (1995). Advanced algorithms for neural networks: A C++ sourcebook. Wiley.
Pillay A. and Wang J. (2003). Modified failure mode and effects analysis using approximate reasoning. Reliability Engineering & System Safety 79: 69–85
Peláez C.E. and Bowles J.B. (1996). Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Information Sciences 88: 177–199
Russomanno, D. J., Bonnell, R. D., & Bowles, J. B. (1992). A blackboard model of an expert system for failure mode and effects analysis. Annual reliability and maintainability symposium, pp. 483–490.
Tay K.M. and Lim C.P. (2006). Fuzzy FMEA with guided rules reduction system for prioritization of failures. International Journal of Quality & Reliability Management 23: 1047–1066
Triantaphyllao, E. (2000). Multi-criteria decision making methods: A comparative study. Kluwer Academic Publishers.
Tummala, R. R. (2000). Fundamentals of microsystems packaging. McGraw-Hill Professional.
Xu K., Tang L.C., Xie M., Ho S.L. and Zhu M.L. (2002). Fuzzy assessment of FMEA for engine systems. Reliability Engineering & System Safety 75: 17–19
Yam Y. (1999). Fuzzy approximation via grid point sampling and singular value decomposition. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics 27: 933–951
About this article
Cite this article
Tay, K.M., Lim, C.P. On the use of fuzzy inference techniques in assessment models: part II: industrial applications. Fuzzy Optim Decis Making 7, 283–302 (2008). https://doi.org/10.1007/s10700-008-9037-y
- Assessment models
- Monotone output property
- Output resolution property
- Failure mode and effect analysis
- Risk priority number