Clustering and visualization of failure modes using an evolving tree
Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2015
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| Online Access: | http://ir.unimas.my/id/eprint/14849/ http://ir.unimas.my/id/eprint/14849/1/Clustering.pdf |
| _version_ | 1848837745360764928 |
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| author | Wui, Lee Chang Tay, Kai Meng |
| author_facet | Wui, Lee Chang Tay, Kai Meng |
| author_sort | Wui, Lee Chang |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community. © 2015 Elsevier Ltd. All rights reserved. |
| first_indexed | 2025-11-15T06:44:32Z |
| format | Article |
| id | unimas-14849 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:44:32Z |
| publishDate | 2015 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-148492021-06-04T09:00:56Z http://ir.unimas.my/id/eprint/14849/ Clustering and visualization of failure modes using an evolving tree Wui, Lee Chang Tay, Kai Meng T Technology (General) Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community. © 2015 Elsevier Ltd. All rights reserved. Elsevier Ltd 2015-05 Article PeerReviewed text en http://ir.unimas.my/id/eprint/14849/1/Clustering.pdf Wui, Lee Chang and Tay, Kai Meng (2015) Clustering and visualization of failure modes using an evolving tree. Expert Systems with Applications, 42 (20). pp. 7235-7244. ISSN 9574174 http://www.scopus.com/inward/record.url?eid=2-s2.0-84937967581&partnerID=40&md5=50c93e87d9cf30df9ec4631d6b589df4 http://dx.doi.org/10.1016/j.eswa.2015.04.036 |
| spellingShingle | T Technology (General) Wui, Lee Chang Tay, Kai Meng Clustering and visualization of failure modes using an evolving tree |
| title | Clustering and visualization of failure modes using an evolving tree |
| title_full | Clustering and visualization of failure modes using an evolving tree |
| title_fullStr | Clustering and visualization of failure modes using an evolving tree |
| title_full_unstemmed | Clustering and visualization of failure modes using an evolving tree |
| title_short | Clustering and visualization of failure modes using an evolving tree |
| title_sort | clustering and visualization of failure modes using an evolving tree |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/14849/ http://ir.unimas.my/id/eprint/14849/ http://ir.unimas.my/id/eprint/14849/ http://ir.unimas.my/id/eprint/14849/1/Clustering.pdf |