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

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 FM...

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Main Authors: Tay, Kai Meng, Chian, Haur Jong, Chee, Peng Lim
Format: E-Article
Language:English
Published: Springer London 2015
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Online Access:http://ir.unimas.my/id/eprint/8146/1/10.1007_s00521-014-1647-4.pdf
http://ir.unimas.my/id/eprint/8146/
http://link.springer.com/article/10.1007%2Fs00521-014-1647-4
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spelling my.unimas.ir.81462015-07-02T05:48:55Z http://ir.unimas.my/id/eprint/8146/ A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry Tay, Kai Meng Chian, Haur Jong Chee, Peng Lim QA76 Computer software 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. Springer London 2015 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/8146/1/10.1007_s00521-014-1647-4.pdf Tay, Kai Meng and Chian, Haur Jong and Chee, Peng Lim (2015) A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry. Neural Computing and Applications, 26 (3). pp. 551-560. ISSN 1433-3058 http://link.springer.com/article/10.1007%2Fs00521-014-1647-4 10.1007/s00521-014-1647-4
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Tay, Kai Meng
Chian, Haur Jong
Chee, Peng Lim
A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
description 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.
format E-Article
author Tay, Kai Meng
Chian, Haur Jong
Chee, Peng Lim
author_facet Tay, Kai Meng
Chian, Haur Jong
Chee, Peng Lim
author_sort Tay, Kai Meng
title A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
title_short A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
title_full A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
title_fullStr A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
title_full_unstemmed A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
title_sort clustering-based failure mode and effect analysis model and its application to the edible bird nest industry
publisher Springer London
publishDate 2015
url http://ir.unimas.my/id/eprint/8146/1/10.1007_s00521-014-1647-4.pdf
http://ir.unimas.my/id/eprint/8146/
http://link.springer.com/article/10.1007%2Fs00521-014-1647-4
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score 13.160551