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Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations

This paper proposed an effort to investigate the suitability of input features and classifier for identifying thermal faults within electrical installations. The features are extracted from the thermal images of electrical equipment and classified using a multilayered perceptron (MLP) artificial neu...

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Main Authors: M. S., Jadin, Kamarul Hawari, Ghazali, Soib, Taib
Format: Article
Published: Elsevier Ltd 2014
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/6342/
http://dx.doi.org/10.1016/j.measurement.2014.07.010
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spelling my.ump.umpir.63422018-02-21T05:10:35Z http://umpir.ump.edu.my/id/eprint/6342/ Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations M. S., Jadin Kamarul Hawari, Ghazali Soib, Taib TK Electrical engineering. Electronics Nuclear engineering This paper proposed an effort to investigate the suitability of input features and classifier for identifying thermal faults within electrical installations. The features are extracted from the thermal images of electrical equipment and classified using a multilayered perceptron (MLP) artificial neural network and support vector machine (SVM). In the experiments, the classification performances from various input features are evaluated. The commonly used classification performance indices, including sensitivity, specificity, accuracy, area under curve (AUC), receiver operating characteristic (ROC) and F-score are employed to identify the most suitable input feature as well as the best configuration of classifiers. The experimental results demonstrate that the combination of features set Tmax, Tdelta and DTbg produce the best input feature for thermal fault detection. In addition, the implementation of SVM using radial basis kernel function (RBF) produces slightly better performance than the MLP artificial neural network. Elsevier Ltd 2014 Article PeerReviewed M. S., Jadin and Kamarul Hawari, Ghazali and Soib, Taib (2014) Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations. Measurement, 57. pp. 15-24. ISSN 0263-2241 http://dx.doi.org/10.1016/j.measurement.2014.07.010 DOI: 10.1016/j.measurement.2014.07.010
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
M. S., Jadin
Kamarul Hawari, Ghazali
Soib, Taib
Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations
description This paper proposed an effort to investigate the suitability of input features and classifier for identifying thermal faults within electrical installations. The features are extracted from the thermal images of electrical equipment and classified using a multilayered perceptron (MLP) artificial neural network and support vector machine (SVM). In the experiments, the classification performances from various input features are evaluated. The commonly used classification performance indices, including sensitivity, specificity, accuracy, area under curve (AUC), receiver operating characteristic (ROC) and F-score are employed to identify the most suitable input feature as well as the best configuration of classifiers. The experimental results demonstrate that the combination of features set Tmax, Tdelta and DTbg produce the best input feature for thermal fault detection. In addition, the implementation of SVM using radial basis kernel function (RBF) produces slightly better performance than the MLP artificial neural network.
format Article
author M. S., Jadin
Kamarul Hawari, Ghazali
Soib, Taib
author_facet M. S., Jadin
Kamarul Hawari, Ghazali
Soib, Taib
author_sort M. S., Jadin
title Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations
title_short Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations
title_full Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations
title_fullStr Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations
title_full_unstemmed Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations
title_sort feature extraction and classification for detecting the thermal faults in electrical installations
publisher Elsevier Ltd
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/6342/
http://dx.doi.org/10.1016/j.measurement.2014.07.010
_version_ 1643665357665206272
score 13.211869

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