Abstract
In traditional Failure Mode and Effect Analysis (FMEA), the Risk Priority Number (RPN) ranking system is used to evaluate the risk level of failures, to rank failures, and to prioritize actions. This approach is simple but it suffers from several weaknesses. In an attempt to overcome the weaknesses associated with the traditional RPN ranking system, several fuzzy inference techniques for RPN determination are investigated in this paper. A generic Fuzzy RPN approach is described, and its performance is evaluated using a case study relating to a semiconductor manufacturing process. In addition, enhancements for the fuzzy RPN approach are proposed by refining the weights of the fuzzy production rules.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ben-Daya, M., and Raouf, A. (1993). “A revi sed failure mode and effects analysis model,” International Journal of Quality & Reliability Management, 3(1):43–7.
Bowles, John B. and Pelæz, C. Enrique (1995), “Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis,” Reliability Engineering & System Safety, Vol. 50, Issue 2, Pages 203–213
Chrysler Corporation, Ford Motor Company, and General Motors Corporation (1995), Potential Failure Mode And Effect analysis (FMEA) Reference Manual.
Guimaræs, Antonio C. F., and Lapa, CelsoMarcelo Franklin (2004), “Effects analysis fuzzy inference system in nuclear problems using approximate reasoning,” Annals of nuclear Energy, vol 31, pp 107–115.
Ireson, G., Coombs, W., Clyde, F., and Richard Y. Moss (1995). Handbook of Reliability Engineering and Management. McGraw-Hill Professional; 2nd edition
Jang, J. S. R., Sun, C. T., and Mizutani, E. (1997). Neural-Fuzzy and soft Computing, Prentice-Hall 1997.
Lin, C. T., and Lee, C. S. G. (1995), Neural Fuzzy Systems, A Neuro-Fuzzy Synergism to Intelligent systems. Prentice-Hall.
Peláez, C. Enrique and Bowles, John B.(1996), “Using fuzzy cognitive maps as a system model for failure modes and effects analysis,” Information Sciences, Volume 88, Issues 1–4, Pages 177–199.
Pillay, Anand and Wang, Jin (2003), “Modifi ed failure mode and effects analysis using approximate reasoning,” Reliability Engineering & System Safety, Volume 79, Issue 1, Pages 69–85.
Xu, L., Tang, L. C., Xie, M., Ho, L. H., and Zhu, M. L (2002). “Fuzzy assessment of FMEA for engine systems,” Reliability Engineering & System Safety, Volume 75, Issue 1, 2002, Pages 17–19.
Yeung, D. S., and Tsang, E. C. C. (1997), “Weighted fuzzy Production rules,” Fuzzy sets and Systems, vol.8, pp.299–313.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Tay, K., Lim, C. (2006). Application of Fuzzy Inference Techniques to FMEA. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_13
Download citation
DOI: https://doi.org/10.1007/3-540-31662-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
eBook Packages: EngineeringEngineering (R0)