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

Tay, K.M and Chian, H.J and Chee, P.L (2014) A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry. Neural Computing and Applications, 26 (3). ISSN 1433-3058

[img]
Preview
PDF
A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry (abstract).pdf

Download (137kB) | Preview
Official URL: http://www.scopus.com/inward/record.url?eid=2-s2.0...

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.

Item Type: Article
Uncontrolled Keywords: Failure mode and effect analysis, Fuzzy ART, Similarity measure, Risk interval measure, Risk ordering, 2014, Engineering, Computer technology, UNIMAS, Universiti Malaysia Sarawak
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 14 Aug 2014 07:00
Last Modified: 24 Oct 2016 01:19
URI: http://ir.unimas.my/id/eprint/4371

Actions (For repository members only: login required)

View Item View Item