single-jc.php

JACIII Vol.15 No.9 pp. 1203-1210
doi: 10.20965/jaciii.2011.p1203
(2011)

Paper:

Enhancing a Fuzzy Failure Mode and Effect Analysis Methodology with an Analogical Reasoning Technique

Tze Ling Jee, Kai Meng Tay, and Chee Khoon Ng

University Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia

Received:
January 31, 2011
Accepted:
July 7, 2011
Published:
November 20, 2011
Keywords:
similarity reasoning, failure mode and effect analysis, fuzzy inference system
Abstract

In this paper, a fuzzy Failure Mode and Effect Analysis (FMEA) methodology incorporating an analogical reasoning technique is presented. FMEA methodology was introduced as a formal and systematic procedure for evaluation of risk associated with potential failure modes in the 1960s. Bowles and Peláez [1] proposed a Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model as an alternative to the conventional RPN model. For an FIS-based RPN (a three-input FIS model), a large set of fuzzy rules are required, and it is tedious to collect the full set of rules. With the grid partition strategy, the number of fuzzy rules required increases in an exponential manner, and this phenomenon is known as the “curse of dimensionality” or the combinatorial rule explosion problem. Hence, a rule selection and similarity reasoning technique, i.e., Approximate Analogical Reasoning Schema (AARS) technique are implemented in a fuzzy FMEA in order to solve the problem. The experiment was conducted using a set of data collected from a semiconductor manufacturing line, i.e., underfill dispensing process, and promising results were obtained.

Cite this article as:
Tze Ling Jee, Kai Meng Tay, and Chee Khoon Ng, “Enhancing a Fuzzy Failure Mode and Effect Analysis Methodology with an Analogical Reasoning Technique,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.9, pp. 1203-1210, 2011.
Data files:
References
  1. [1] J. B. Bowles and C. E. Peláez, “Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis,” Reliability Engineering & System Safety, Vol.50, pp. 203-213, 1995.
  2. [2] K. M. Tay and C. P. Lim, “Enhancing the Failure Mode and Effect Analysis Methodology with fuzzy inference techniques,” J. of Intelligent & Fuzzy Systems, Vol.21 No.1-2, pp. 135-146, 2010.
  3. [3] A.Pillay and J. Wang, “Modified failure mode and Effects analysis using approximate reasoning,” Reliability Engineering & System Safety, Vol.79, pp. 69-85, 2003.
  4. [4] A. C. F. Guimarães and C. M. F. Lapa, “Effects analysis fuzzy inference system in nuclear problems using Approximate reasoning,” Annals of nuclear Energy, Vol.31, pp. 107-115, 2004a.
  5. [5] A. C. F. Guimarães and C. M. F. Lapa, “Fuzzy FMEA applied to PWR chemical and volume control system,” Progress in Nuclear Energy, Vol.44, pp. 191-213, 2004.
  6. [6] K. M. Tay and C. P Lim, “On the Use of Fuzzy Inference Techniques in Assessment Models: Part II: Industrial Applications,” Fuzzy Optim Decis Making, pp. 283-302, 2008.
  7. [7] R. J. Latino, “Optimizing FMEA and RCA efforts in health care,” ASHRM Journal, Vol.24, No.3, pp. 21-28, 2004.
  8. [8] Z. Yang, S. Bonsall, and J. Wang, “Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA,” IEEE Trans. On Reliability, Vol.57, No.3, pp. 517-528, 2008.
  9. [9] K. M. Tay and C. P. Lim, “Fuzzy FMEA with Guided Rules Reduction System for Prioritization of Failures,” Int. J. of Quality & Reliability Management, Vol.23, pp. 1047-1066, 2006.
  10. [10] Y. M.Wang, K. S. Chin, G. K. K. Poon, and J. B. Yang, “Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean,” Expert Systems with Applications, Vol.36, pp. 1195-1207, 2009.
  11. [11] R. K. Sharma, D. Kumar, and P. Kumar, “Systematic failure mode analysis (FMEA) using fuzzy linguistic modeling,” Int. J. of Quality & Reliabilty Management, Vol.22, No.9, pp. 986-1004, 2005.
  12. [12] Y. M. Wang, K. S. Chin, G. K. K. Poon, and J. B. Yang, “Failure mode and effects analysis using a group-based evidential reasoning approach,” Computers & Operations Research, Vol.36, pp. 1768-1779, 2009.
  13. [13] Y. Jin, “Fuzzy Modeling of High-Dimensional Systems: Complexity Reduction and Interpretability Improvement,” IEEE Trans on Fuzzy Systems, Vol.8, No.2, pp. 212-221, 2000.
  14. [14] Y. Yam, P. Baranyi, and C. T. Yang, “Reduction of fuzzy rule base via singular value decomposition,” IEEE Trans on Fuzzy Systems, Vol.7, No.2, pp. 120-132, 1999.
  15. [15] Y. Yam, “Fuzzy approximation via grid point sampling and singular value decomposition,” IEEE Trans. on Systems, Man, and Cybernetics Part B: Cybernetics, Vol.27, pp. 933-951, 1999.
  16. [16] M. Setnes and R. Babu��ska, “Rule Base Reduction: Some Comments on the Use of Orthogonal Transforms,” IEEE Trans. on Systems, Man, and Cybernetics – Part C, Vol.31, No.2, pp. 199-206, 2001.
  17. [17] I. B. Turksen, and Z. Zhong, “An approximate Analogical Reasoning Approach Based on Similarity measures,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.18, No.6, pp. 1049-1056, 1988.
  18. [18] L. T. Kóczy and K. Hirota, “Size Reduction by Interpolation in Fuzzy Rule Bases,” IEEE Trans. on Systems, Man, and Cybernetics – Part B, Vol.27, No.1, pp. 14-25, 1997.
  19. [19] Z. H. Huang and Q. Shen, “Fuzzy interpolation and extrapolation: a practical approach,” IEEE Trans Fuzzy System, Vol.16, pp. 13-28, 2008.
  20. [20] K. M. Tay and C. P. Lim, “On the Use of Fuzzy Rule Interpolation Techniques for Monotonic Multi-Input Fuzzy Rule Base Models,” FUZZ-IEEE 2009, pp. 1736-1740, 2009.
  21. [21] S. Guillaume, “Designing Fuzzy Inference Systems from Data: An Interpretability-Oriented Review,” IEEE Trans on Fuzzy Systems, Vol.9, No.3, pp. 426-443, 2001.
  22. [22] J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.23, No.3, pp. 665-685, 1993.
  23. [23] R. P. Hall, “Computational Approaches to Analogical Reasoning: A Comparative Analysis,” Artificial Intelligence, pp. 39-120, 1989.
  24. [24] R. R. Tummala, “Fundamentals of Microsystems packaging,” McGraw-Hill Professional, 2000.
  25. [25] J. Kennedy & R. C. Eberhart, “Particle Swarm Optimization,” In Proc. IEEE Int. Conf. on Neural Networks, Vol.4, pp. 1942-1948, 1995.
  26. [26] Z. W. Geem, “Music-Inspired Harmony Search Algorithm: Theory and Applications,” Springer, 2009.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021