A Single Input Rule Modules Connected Fuzzy FMEA Methodology for Edible Bird Nest Processing

  • Chian Haur Jong
  • Kai Meng Tay
  • Chee Peng Lim
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


Despite of the popularity of the fuzzy Failure Mode and Effects Analysis (FMEA) methodology, there are several limitations in combining the Fuzzy Inference System (FIS) and the Risk Priority Number (RPN) model. Two main limitations are: (1) it is difficult and impractical to form a complete fuzzy rule base when the number of required rules is large; and (2) fulfillment of the monotonicity property is a difficult problem. In this paper, a new fuzzy FMEA methodology with a zero-order Single Input Rule Modules (SIRMs) connected FIS-based RPN model is proposed. An SIRMs connected FIS is adopted as an alternative to the traditional FIS to reduce the number of fuzzy rules required in the modeling process. To preserve the monotonicity property of the SIRMs-connected FIS-based RPN model, a number of theorems in the literature are simplified and adopted as the governing equations for the proposed fuzzy FMEA methodology. A case study relating to edible bird nest (EBN) processing in Sarawak (together with Sabah, known as the world’s number two source area of bird nest after Indonesia) is reported. In short, the findings in this paper contribute towards building a new fuzzy FMEA methodology using the SIRM s connected FIS-based RPN model. Besides that, the usefulness of the simplified theorems in a practical FMEA application is demonstrated.


Failure Mode and Effect Analysis Fuzzy reasoning Single-input-rule-modules connected fuzzy inference system Mmonotonicity property Edible bird nest 


  1. 1.
    Xu, K., Tang, L.C., Xie, M., Ho, S.L., Zhu, M.L.: Fuzzy assessment of FMEA for engine systems. Reliab. Eng. Systety Safe. 75, 17–29 (2002)CrossRefGoogle Scholar
  2. 2.
    Wang, Y.M., Chin, K.S., Poon, G.K.K., Yang, J.B.: Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Syst. Appl. 36, 1195–1207 (2009)CrossRefGoogle Scholar
  3. 3.
    Liu, J., Martínez, L., Wang, H., Rodríguez, R.M., Novozhilov, V.: Computing with words in risk assessment. Int. J. Comput. Int. Syst. 3, 396–419 (2010)Google Scholar
  4. 4.
    Tay, K.M., Lim, C.P.: Fuzzy FMEA with a guided rules reduction system for prioritization of failures. Int. J. Qual. Reliab. Manag. 23, 1047–1066 (2006)CrossRefGoogle Scholar
  5. 5.
    Bowles, J.B., Pelaez, C.E.: Fuzzy logic prioritization of failures in a system failure mode, effect and criticality analysis. Reliab. Eng. Syst. Safety 50, 203–213 (1995)CrossRefGoogle Scholar
  6. 6.
    Guimarães, A.C.F., Lapa, C.M.F.: Fuzzy FMEA applied to PWR chemical and volume control system. Progr. Nucl. Energy 44, 191–213 (2004)CrossRefGoogle Scholar
  7. 7.
    Pillay, A., Wang, J.: Modified failure mode and effects analysis using approximate reasoning. Reliab. Eng. Syst. Safe. 79, 69–85 (2003)CrossRefGoogle Scholar
  8. 8.
    Yang, Z., Bonsall, S., Wang, J.: Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA. IEEE Trans. Reliab. 57, 517–528 (2008)CrossRefGoogle Scholar
  9. 9.
    Jin, Y.C.: Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Trans. Fuzzy Syst. 2, 212–21 (2000)CrossRefGoogle Scholar
  10. 10.
    Tay, K.M., Lim, C.P.: On monotonic sufficient conditions of fuzzy inference systems and their applications. Int. J. Uncertain. Fuzz. 19, 731–757 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Tay, K.M., Lim, C.P.: On the use of fuzzy inference techniques in assessment models: part I— theoretical properties. Fuzzy Optim. Decis. Making 7, 269–281 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Yubazaki, N., Yi, J.Q., Hirota, K.: SIRMs (single input rule modules) connected fuzzy inference model. J. Adv. Comput. Intell. 1, 22–29 (1997)Google Scholar
  14. 14.
    Seki, H., Ishii, H., Mizumoto, M.: On the generalization of single input rule modules connected type fuzzy reasoning method. IEEE Trans. Fuzzy Syst. 16, 1180–1187 (2008)CrossRefGoogle Scholar
  15. 15.
    Seki, H., Ishii, H., Mizumoto, M.: On the monotonicity of fuzzy-inference method related to T-S inference method. IEEE Trans. Fuzzy Syst. 18, 629–634 (2010)CrossRefGoogle Scholar
  16. 16.
    Seki, H., Tay, K.M.: On the monotonicity of fuzzy inference models. J. Adv. Comput. Intell. Intell. Inform. 16, 592–602 (2012)Google Scholar
  17. 17.
    Tay, K.M., Lim, C.P.: Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems. In: IEEE International Conference on Fuzzy Systems, pp. 1219–1224 (2011)Google Scholar
  18. 18.
    Tay, K.M., Lim, C.P., Teh, C.Y., Lau, S.H.: A monotonicity index for the monotone fuzzy modeling problem. In: IEEE International Conference on Fuzzy Systems, pp. 1–8 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chian Haur Jong
    • 1
  • Kai Meng Tay
    • 1
  • Chee Peng Lim
    • 2
  1. 1.Universiti Malaysia SarawakKota SamarahanMalaysia
  2. 2.Centre for Intelligent Systems ResearchDeakin UniversityGeelongAustralia

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