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Application of self-organizing map to failure modes and effects analysis methodology

In this paper, a self-organizing map (SOM) neural network is used to visualize corrective actions of failure modes and effects analysis (FMEA). SOM is a popular unsupervised neural network model that aims to produce a low-dimensional map (typically a two-dimensional map) for visualizing high-dimensi...

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Main Authors: Chang, Wuilee, Liew, Meng Pang, Tay, Kai Meng
Format: E-Article
Language:English
Published: Elsevier 2017
Subjects:
QA Mathematics
Online Access:http://ir.unimas.my/id/eprint/15892/7/Application%20of%20self-organizing%20map%20to%20failure%20modes%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/15892/
http://www.sciencedirect.com/science/article/pii/S0925231217305702
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spelling my.unimas.ir.158922017-06-15T03:38:44Z http://ir.unimas.my/id/eprint/15892/ Application of self-organizing map to failure modes and effects analysis methodology Chang, Wuilee Liew, Meng Pang Tay, Kai Meng QA Mathematics In this paper, a self-organizing map (SOM) neural network is used to visualize corrective actions of failure modes and effects analysis (FMEA). SOM is a popular unsupervised neural network model that aims to produce a low-dimensional map (typically a two-dimensional map) for visualizing high-dimensional data. With regards to FMEA, it is a popular methodology to identify potential failure modes for a product or a process, to assess the risk associated with those failure modes, also, to identify and carry out corrective actions to address the most serious concerns. Despite the popularity of 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. The use of SOM in FMEA is new. In this paper, corrective actions in FMEA are described in their severity, occurrence and detect scores. SOM is then used as a visualization aid for FMEA users to see the relationship among corrective actions via a map. Color information from the SOM map is then included to the FMEA worksheet for better visualization. In addition, a Risk Priority Number Interval is used to allow corrective actions to be evaluated and ordered in groups. Such approach provides a quick and easily understandable framework to elucidate important information from a complex FMEA worksheet; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is two-fold, viz., the use of SOM as an effective neural network learning paradigm to facilitate FMEA implementations, and the use of a computational visualization approach to tackle the two well-known shortcomings of FMEA. Elsevier 2017-03-28 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/15892/7/Application%20of%20self-organizing%20map%20to%20failure%20modes%20%28abstract%29.pdf Chang, Wuilee and Liew, Meng Pang and Tay, Kai Meng (2017) Application of self-organizing map to failure modes and effects analysis methodology. Neurocomputing, 2017. ISSN 0925-2312 http://www.sciencedirect.com/science/article/pii/S0925231217305702 10.1016/j.neucom.2016.04.073
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 QA Mathematics
spellingShingle QA Mathematics
Chang, Wuilee
Liew, Meng Pang
Tay, Kai Meng
Application of self-organizing map to failure modes and effects analysis methodology
description In this paper, a self-organizing map (SOM) neural network is used to visualize corrective actions of failure modes and effects analysis (FMEA). SOM is a popular unsupervised neural network model that aims to produce a low-dimensional map (typically a two-dimensional map) for visualizing high-dimensional data. With regards to FMEA, it is a popular methodology to identify potential failure modes for a product or a process, to assess the risk associated with those failure modes, also, to identify and carry out corrective actions to address the most serious concerns. Despite the popularity of 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. The use of SOM in FMEA is new. In this paper, corrective actions in FMEA are described in their severity, occurrence and detect scores. SOM is then used as a visualization aid for FMEA users to see the relationship among corrective actions via a map. Color information from the SOM map is then included to the FMEA worksheet for better visualization. In addition, a Risk Priority Number Interval is used to allow corrective actions to be evaluated and ordered in groups. Such approach provides a quick and easily understandable framework to elucidate important information from a complex FMEA worksheet; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is two-fold, viz., the use of SOM as an effective neural network learning paradigm to facilitate FMEA implementations, and the use of a computational visualization approach to tackle the two well-known shortcomings of FMEA.
format E-Article
author Chang, Wuilee
Liew, Meng Pang
Tay, Kai Meng
author_facet Chang, Wuilee
Liew, Meng Pang
Tay, Kai Meng
author_sort Chang, Wuilee
title Application of self-organizing map to failure modes and effects analysis methodology
title_short Application of self-organizing map to failure modes and effects analysis methodology
title_full Application of self-organizing map to failure modes and effects analysis methodology
title_fullStr Application of self-organizing map to failure modes and effects analysis methodology
title_full_unstemmed Application of self-organizing map to failure modes and effects analysis methodology
title_sort application of self-organizing map to failure modes and effects analysis methodology
publisher Elsevier
publishDate 2017
url http://ir.unimas.my/id/eprint/15892/7/Application%20of%20self-organizing%20map%20to%20failure%20modes%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/15892/
http://www.sciencedirect.com/science/article/pii/S0925231217305702
_version_ 1644512253547905024
score 13.211869

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