Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network

As a major industry prime mover, induction motor plays an important role in manufacturing. In fact, production can cease its operation if there is some error or fault in the induction motor. In the industry, bearing, stator and rotor fault are the highest among other faults. Thus, this paper is to c...

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Main Authors: Talib, M. F., Othman, M. F., Azli, N. H. N.
Format: Article
Published: Universiti Teknikal Malaysia Melaka 2016
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Online Access:http://eprints.utm.my/id/eprint/71683/
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.716832017-11-16T06:05:02Z http://eprints.utm.my/id/eprint/71683/ Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network Talib, M. F. Othman, M. F. Azli, N. H. N. TA Engineering (General). Civil engineering (General) As a major industry prime mover, induction motor plays an important role in manufacturing. In fact, production can cease its operation if there is some error or fault in the induction motor. In the industry, bearing, stator and rotor fault are the highest among other faults. Thus, this paper is to compare the accuracy of bearing, stator and rotor fault classification between General Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) with the previous work using Principle Component Analysis (PCA). The accuracy of fault classification for each method is improved by the selection of features extraction and number of classification. The features extraction used are mean, root mean square, skewness, kurtosis and crest factor. The sample data has been taken from Machinery Fault Simulator using accelerometer sensor, logged to text file using Labview software and analysed by using Matlab software. The accuracy of fault classification using GRNN method is higher than PNN because the sample data is classified through the regression of data as long as the sample data is redundant and lies on the regression distribution. Universiti Teknikal Malaysia Melaka 2016 Article PeerReviewed Talib, M. F. and Othman, M. F. and Azli, N. H. N. (2016) Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network. Journal of Telecommunication, Electronic and Computer Engineering, 8 (11). pp. 93-98. ISSN 2180-1843 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011407725&partnerID=40&md5=9bff154764988e4b3c215e3602dbbbce
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Talib, M. F.
Othman, M. F.
Azli, N. H. N.
Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network
description As a major industry prime mover, induction motor plays an important role in manufacturing. In fact, production can cease its operation if there is some error or fault in the induction motor. In the industry, bearing, stator and rotor fault are the highest among other faults. Thus, this paper is to compare the accuracy of bearing, stator and rotor fault classification between General Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) with the previous work using Principle Component Analysis (PCA). The accuracy of fault classification for each method is improved by the selection of features extraction and number of classification. The features extraction used are mean, root mean square, skewness, kurtosis and crest factor. The sample data has been taken from Machinery Fault Simulator using accelerometer sensor, logged to text file using Labview software and analysed by using Matlab software. The accuracy of fault classification using GRNN method is higher than PNN because the sample data is classified through the regression of data as long as the sample data is redundant and lies on the regression distribution.
format Article
author Talib, M. F.
Othman, M. F.
Azli, N. H. N.
author_facet Talib, M. F.
Othman, M. F.
Azli, N. H. N.
author_sort Talib, M. F.
title Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network
title_short Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network
title_full Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network
title_fullStr Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network
title_full_unstemmed Classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network
title_sort classification of machine fault using principle component analysis, general regression neural network and probabilistic neural network
publisher Universiti Teknikal Malaysia Melaka
publishDate 2016
url http://eprints.utm.my/id/eprint/71683/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011407725&partnerID=40&md5=9bff154764988e4b3c215e3602dbbbce
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