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

Tay,KM, Jong,CH and Lim,CP 2015, A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry, Neural Computing and Applications, vol. 26, no. 3, pp. 551-560, doi: 10.1007/s00521-014-1647-4.

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Title A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
Author(s) Tay,KM
Jong,CH
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Journal name Neural Computing and Applications
Volume number 26
Issue number 3
Start page 551
End page 560
Total pages 10
Publisher Springer U K
Place of publication London, United Kingdom
Publication date 2015-07-01
ISSN 0941-0643
1433-3058
Keyword(s) Failure mode and effect analysis
Fuzzy ART
Risk interval measure
Risk ordering
Similarity measure
Summary 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. © 2014 The Natural Computing Applications Forum.
Language eng
DOI 10.1007/s00521-014-1647-4
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Springer U K
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070578

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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