caa a22 4500 606159592 CHVBK 20210128100625.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1647-4 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1647-4 A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry [Elektronische Daten] [Kai Tay, Chian Jong, Chee Lim] 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. The Natural Computing Applications Forum, 2014 Failure mode and effect analysis nationallicence Fuzzy ART nationallicence Similarity measure nationallicence Risk interval measure nationallicence Risk ordering nationallicence Tay Kai Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia aut Jong Chian Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia aut Lim Chee Centre for Intelligent Systems Research, Deakin University, Victoria, Australia aut Neural Computing and Applications Springer London 26/3(2015-04-01), 551-560 0941-0643 26:3<551 2015 26 521 https://doi.org/10.1007/s00521-014-1647-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1647-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tay Kai Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia aut NATIONALLICENCE 700 1- Jong Chian Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia aut NATIONALLICENCE 700 1- Lim Chee Centre for Intelligent Systems Research, Deakin University, Victoria, Australia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 551-560 0941-0643 26:3<551 2015 26 521