Advertisement

Neural Computing and Applications

, Volume 26, Issue 3, pp 551–560 | Cite as

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

  • Kai Meng Tay
  • Chian Haur Jong
  • Chee Peng Lim
Advances in Intelligent Data Processing and Analysis

Abstract

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups.

Keywords

Failure mode and effect analysis Fuzzy ART Similarity measure Risk interval measure Risk ordering 

Notes

Acknowledgments

The authors gratefully acknowledge the Exploratory Research Grant Scheme (ERGS/1/11/TK/UNIMAS/03/05), Fundamental Research Grant Scheme (FRGS/ICT02(01)/997/2013(38)), and Research Acculturation Collaborative Effort (RACE/F2/TK/UNIMAS/5) for supporting this research work and Universiti Malaysia Sarawak for the facilities.

References

  1. 1.
    Stamatis DH (2003) Failure mode and effect analysis: FMEA from theory to execution. ASQ PressGoogle Scholar
  2. 2.
    Bowles JB, Peláez CE (1995) Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab Eng Syst Safe 50:203–213CrossRefGoogle Scholar
  3. 3.
    Guimarães ACF, Lapa CMF (2004) Fuzzy FMEA applied to PWR chemical and volume control system. Prog Nucl Energy 44:191–213CrossRefGoogle Scholar
  4. 4.
    Zafiropoulos EP, Dialynas EN (2005) Reliability prediction and failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic. Int J Qual Reliab Manag 22:183–200CrossRefGoogle Scholar
  5. 5.
    Tay KM, Lim CP (2006) Fuzzy FMEA with a guided rules reduction system for prioritization of failures. Int J Qual Reliab Manag 23:1047–1066CrossRefGoogle Scholar
  6. 6.
    Garrick BJ (1988) The approach to risk analysis in three industries: nuclear power, space systems, and chemical process. Reliab Eng Syst Safe 23:195–205CrossRefGoogle Scholar
  7. 7.
    Korayem MH, Iravani A (2008) Improvement of 3P and 6R mechanical robots reliability and quality applying FMEA and QFD approaches. Robot Comput Integr Manuf 24:472–487CrossRefGoogle Scholar
  8. 8.
    McNally KM, Page MA, Sunderland VB (1997) Failure-mode and effects analysis in improving a drug distribution system. Am J Health Syst Pharm 54:171–177Google Scholar
  9. 9.
    Kahraman C, Kaya İ, Şenvar Ö (2013) Healthcare failure mode and effects analysis under fuzziness. Hum Ecol Risk Assess 19:538–552CrossRefGoogle Scholar
  10. 10.
    Geum Y, Shin J, Park Y (2011) FMEA-based portfolio approach to service productivity improvement. Serv Ind J 31:1825–1847CrossRefGoogle Scholar
  11. 11.
    Jong CH, Tay KM, Lim CP (2013) Application of the fuzzy failure mode and effect analysis methodology to edible bird nest processing. Comput Electron Agric 96:90–108CrossRefGoogle Scholar
  12. 12.
    Jong CH, Tay KM, Lim CP (2014) A single input rule modules connected fuzzy FMEA methodology for edible bird nest processing. In: Snášel V, Krömer P, Köppen M, Schaefer G (eds) Soft computing in industrial applications, vol 223. Springer, Switzerland, pp 165–176 Google Scholar
  13. 13.
    Helvacioglu S, Ozen E (2014) Fuzzy based failure modes and effect analysis for yacht system design. Ocean Eng. doi: 10.1016/j.oceaneng.2013.12.015 Google Scholar
  14. 14.
    Zaman MB, Kobayashi E, Wakabayashi N, Khanfir S, Pitana T, Maimun A (2014) Fuzzy FMEA model for risk evaluation of ship collisions in the Malacca Strait: based on AIS data. J Simul 8:91–104CrossRefGoogle Scholar
  15. 15.
    Liu HC, Liu L, Liu N (2013) Risk evaluation approaches in failure mode and effects analysis: a literature review. Expert Syst Appl 40:828–838CrossRefGoogle Scholar
  16. 16.
    Tay KM, Lim CP (2008) On the use of fuzzy inference techniques in assessment models: part I—theoretical properties. Fuzzy Optim Decis Mak 7:269–281CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Tay KM, Lim CP (2008) On the use of fuzzy inference techniques in assessment models: part II—industrial applications. Fuzzy Optim Decis Mak 7:283–302CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Tay KM, Lim CP (2011) On monotonic sufficient conditions of fuzzy inference systems and their applications. Int J Uncertainty Fuzziness Knowl Based Syst 19:731–757CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Keskin GA, Özkan C (2009) An alternative evaluation of FMEA: fuzzy art algorithm. Qual Reliab Eng Int 25:647–661CrossRefGoogle Scholar
  20. 20.
    Carpenter GA, Grossberg S, Rosen DB (1991) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4:759–771CrossRefGoogle Scholar
  21. 21.
    Mendel J, Wu D (2010) Perceptual computing: aiding people in making subjective judgments. Wiley, Hoboken, New JerseyCrossRefGoogle Scholar
  22. 22.
    Rui X, Donald CW (2009) Clustering. IEEE Press/Wiley, Hoboken, New JerseyGoogle Scholar
  23. 23.
    MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp 281–297Google Scholar
  24. 24.
    Carpenter GA, Grossberg S, Rosen DB (1991) ART 2-A: an adaptive resonance algorithm for rapid category learning and recognition. Neural Netw 4:493–504CrossRefGoogle Scholar
  25. 25.
    Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13:517–530CrossRefMathSciNetGoogle Scholar
  26. 26.
    Jang JSR, Sun CT, Mizutani E (1997) Neural-fuzzy and soft computing. Prentice-Hall, Upper Saddle River, New JerseyGoogle Scholar
  27. 27.
    Hobbs JJ (2004) Problems in the harvest of edible birds’ nests in Sarawak and Sabah, Malaysian Borneo. Biodivers Conserv 13:2209–2226CrossRefGoogle Scholar
  28. 28.
    Jordan D (2009) Globalization and bird’s nest soup. Int Dev Plan Rev 26:97–110CrossRefGoogle Scholar
  29. 29.
    Kohonen T (2001) Self organizing maps, 3rd edn. Springer, BerlinCrossRefzbMATHGoogle Scholar
  30. 30.
    Pakkanen J, Iivarinen J, Oja E (2006) The evolving tree-analysis and applications. IEEE Trans Neural Netw 17:591–603CrossRefGoogle Scholar
  31. 31.
    Chang WL, Tay KM, Lim CP (2014) A new evolving tree for text document clustering and visualization. In: Snášel V, Krömer P, Köppen M, Schaefer G (eds) Soft computing in industrial applications, vol 223. Springer, Switzerland, pp 141–151Google Scholar
  32. 32.
    Jee TL, Tay KM, Ng CK (2013) A new fuzzy criterion-referenced assessment with a fuzzy rule selection technique and a monotonicity-preserving similarity reasoning scheme. J Intell Fuzzy Syst 24:261–279zbMATHMathSciNetGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2014

Authors and Affiliations

  • Kai Meng Tay
    • 1
  • Chian Haur Jong
    • 1
  • Chee Peng Lim
    • 2
  1. 1.Faculty of EngineeringUniversiti Malaysia SarawakKota SamarahanMalaysia
  2. 2.Centre for Intelligent Systems ResearchDeakin UniversityVictoriaAustralia

Personalised recommendations