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.
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Stamatis DH (2003) Failure mode and effect analysis: FMEA from theory to execution. ASQ Press
Bowles JB, Peláez CE (1995) Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab Eng Syst Safe 50:203–213
Guimarães ACF, Lapa CMF (2004) Fuzzy FMEA applied to PWR chemical and volume control system. Prog Nucl Energy 44:191–213
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 22:183–200
Tay KM, Lim CP (2006) Fuzzy FMEA with a guided rules reduction system for prioritization of failures. Int J Qual Reliab Manag 23:1047–1066
Garrick BJ (1988) The approach to risk analysis in three industries: nuclear power, space systems, and chemical process. Reliab Eng Syst Safe 23:195–205
Korayem MH, Iravani A (2008) Improvement of 3P and 6R mechanical robots reliability and quality applying FMEA and QFD approaches. Robot Comput Integr Manuf 24:472–487
McNally KM, Page MA, Sunderland VB (1997) Failure-mode and effects analysis in improving a drug distribution system. Am J Health Syst Pharm 54:171–177
Kahraman C, Kaya İ, Şenvar Ö (2013) Healthcare failure mode and effects analysis under fuzziness. Hum Ecol Risk Assess 19:538–552
Geum Y, Shin J, Park Y (2011) FMEA-based portfolio approach to service productivity improvement. Serv Ind J 31:1825–1847
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 96:90–108
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
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
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 8:91–104
Liu HC, Liu L, Liu N (2013) Risk evaluation approaches in failure mode and effects analysis: a literature review. Expert Syst Appl 40:828–838
Tay KM, Lim CP (2008) On the use of fuzzy inference techniques in assessment models: part I—theoretical properties. Fuzzy Optim Decis Mak 7:269–281
Tay KM, Lim CP (2008) On the use of fuzzy inference techniques in assessment models: part II—industrial applications. Fuzzy Optim Decis Mak 7:283–302
Tay KM, Lim CP (2011) On monotonic sufficient conditions of fuzzy inference systems and their applications. Int J Uncertainty Fuzziness Knowl Based Syst 19:731–757
Keskin GA, Özkan C (2009) An alternative evaluation of FMEA: fuzzy art algorithm. Qual Reliab Eng Int 25:647–661
Carpenter GA, Grossberg S, Rosen DB (1991) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4:759–771
Mendel J, Wu D (2010) Perceptual computing: aiding people in making subjective judgments. Wiley, Hoboken, New Jersey
Rui X, Donald CW (2009) Clustering. IEEE Press/Wiley, Hoboken, New Jersey
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 281–297
Carpenter GA, Grossberg S, Rosen DB (1991) ART 2-A: an adaptive resonance algorithm for rapid category learning and recognition. Neural Netw 4:493–504
Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13:517–530
Jang JSR, Sun CT, Mizutani E (1997) Neural-fuzzy and soft computing. Prentice-Hall, Upper Saddle River, New Jersey
Hobbs JJ (2004) Problems in the harvest of edible birds’ nests in Sarawak and Sabah, Malaysian Borneo. Biodivers Conserv 13:2209–2226
Jordan D (2009) Globalization and bird’s nest soup. Int Dev Plan Rev 26:97–110
Kohonen T (2001) Self organizing maps, 3rd edn. Springer, Berlin
Pakkanen J, Iivarinen J, Oja E (2006) The evolving tree-analysis and applications. IEEE Trans Neural Netw 17:591–603
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 141–151
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 24:261–279
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.
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Tay, K.M., Jong, C.H. & Lim, C.P. A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry. Neural Comput & Applic 26, 551–560 (2015). https://doi.org/10.1007/s00521-014-1647-4
- Failure mode and effect analysis
- Fuzzy ART
- Similarity measure
- Risk interval measure
- Risk ordering