A Naïve-Bayes classifier for damage detection in engineering materials

This paper is intended to introduce the Bayesian network in general and the Naïve-Bayes classifier in particular as one of the most successful classification systems to simulate damage detection in engineering materials. A method for feature subset selection has also been introduced too. The method...

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Main Authors: Addin, O., Salit, Mohd Sapuan, Mahdi Ahmad Saad, Elsadig, Othman, Mohamed
Format: Article
Language:English
English
Published: Elsevier 2007
Subjects:
Online Access:http://psasir.upm.edu.my/id/eprint/12500/
http://psasir.upm.edu.my/id/eprint/12500/1/A%20Na%C3%AFve.pdf
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author Addin, O.
Salit, Mohd Sapuan
Mahdi Ahmad Saad, Elsadig
Othman, Mohamed
author_facet Addin, O.
Salit, Mohd Sapuan
Mahdi Ahmad Saad, Elsadig
Othman, Mohamed
author_sort Addin, O.
building UPM Institutional Repository
collection Online Access
description This paper is intended to introduce the Bayesian network in general and the Naïve-Bayes classifier in particular as one of the most successful classification systems to simulate damage detection in engineering materials. A method for feature subset selection has also been introduced too. The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naïve-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. The data sets were conducted based on artificial damages created in quasi isotopic laminated composites of the AS4/3501-6 graphite/epoxy system and ball bearing of the type 6204 with a steel cage. The Naïve-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials.
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spelling upm-125002015-12-08T07:51:11Z http://psasir.upm.edu.my/id/eprint/12500/ A Naïve-Bayes classifier for damage detection in engineering materials Addin, O. Salit, Mohd Sapuan Mahdi Ahmad Saad, Elsadig Othman, Mohamed This paper is intended to introduce the Bayesian network in general and the Naïve-Bayes classifier in particular as one of the most successful classification systems to simulate damage detection in engineering materials. A method for feature subset selection has also been introduced too. The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naïve-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. The data sets were conducted based on artificial damages created in quasi isotopic laminated composites of the AS4/3501-6 graphite/epoxy system and ball bearing of the type 6204 with a steel cage. The Naïve-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials. Elsevier 2007 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/12500/1/A%20Na%C3%AFve.pdf Addin, O. and Salit, Mohd Sapuan and Mahdi Ahmad Saad, Elsadig and Othman, Mohamed (2007) A Naïve-Bayes classifier for damage detection in engineering materials. Materials and Design, 28 (8). pp. 2379-2386. ISSN 0264-1275 http://dx.doi.org/10.1016/j.matdes.2006.07.018 Bayesian statistical decision theory Bayesian field theory English
spellingShingle Bayesian statistical decision theory
Bayesian field theory
Addin, O.
Salit, Mohd Sapuan
Mahdi Ahmad Saad, Elsadig
Othman, Mohamed
A Naïve-Bayes classifier for damage detection in engineering materials
title A Naïve-Bayes classifier for damage detection in engineering materials
title_full A Naïve-Bayes classifier for damage detection in engineering materials
title_fullStr A Naïve-Bayes classifier for damage detection in engineering materials
title_full_unstemmed A Naïve-Bayes classifier for damage detection in engineering materials
title_short A Naïve-Bayes classifier for damage detection in engineering materials
title_sort naïve-bayes classifier for damage detection in engineering materials
topic Bayesian statistical decision theory
Bayesian field theory
url http://psasir.upm.edu.my/id/eprint/12500/
http://psasir.upm.edu.my/id/eprint/12500/
http://psasir.upm.edu.my/id/eprint/12500/1/A%20Na%C3%AFve.pdf