Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography

Infrared thermographic inspection system is widely being utilized for defect detection in electrical equipment. Conventional inspection based on the temperature data interpretation and evaluation the condition of the equipment is subjective and depends on the human experts. Implementation of an auto...

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Main Author:	Huda, A. S. Nazmul
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/46367/1/A.%20S.%20Nazmul%20Huda24.pdf
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id	my-usm-ep.46367
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spelling	my-usm-ep.463672020-02-27T08:07:50Z Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography 2013-12 Huda, A. S. Nazmul TK1-9971 Electrical engineering. Electronics. Nuclear engineering Infrared thermographic inspection system is widely being utilized for defect detection in electrical equipment. Conventional inspection based on the temperature data interpretation and evaluation the condition of the equipment is subjective and depends on the human experts. Implementation of an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In this thesis, an automatic features extraction system from thermal image of defects and the intelligent classification of thermal condition based on neural network are proposed. The proposed system extracts first order histogram based features and grey level co-occurrence matrix features from the segmented regions and evaluates the effectiveness of these features for defect characterization. Three feature selection techniques namely principal component analysis, the discriminant analysis and individual feature performance analysis are employed to find out the useful and important statistical features. In this study, multilayered perceptron network is proposed for classifying thermal condition into two classes namely normal and defective. The multilayered perceptron neural networks are trained using various training algorithms. Additionally, the present research introduces a computer aided defect diagnosis system where the defected region is found by manual thresholding and intensity features are extracted from each segmented region. The results prove that the statistical features are capable to classify thermal condition and the neural networks achieve the accuracy around 73~78% 2013-12 Thesis http://eprints.usm.my/46367/ http://eprints.usm.my/46367/1/A.%20S.%20Nazmul%20Huda24.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Huda, A. S. Nazmul Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography
description	Infrared thermographic inspection system is widely being utilized for defect detection in electrical equipment. Conventional inspection based on the temperature data interpretation and evaluation the condition of the equipment is subjective and depends on the human experts. Implementation of an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In this thesis, an automatic features extraction system from thermal image of defects and the intelligent classification of thermal condition based on neural network are proposed. The proposed system extracts first order histogram based features and grey level co-occurrence matrix features from the segmented regions and evaluates the effectiveness of these features for defect characterization. Three feature selection techniques namely principal component analysis, the discriminant analysis and individual feature performance analysis are employed to find out the useful and important statistical features. In this study, multilayered perceptron network is proposed for classifying thermal condition into two classes namely normal and defective. The multilayered perceptron neural networks are trained using various training algorithms. Additionally, the present research introduces a computer aided defect diagnosis system where the defected region is found by manual thresholding and intensity features are extracted from each segmented region. The results prove that the statistical features are capable to classify thermal condition and the neural networks achieve the accuracy around 73~78%
format	Thesis
qualification_level	Master's degree
author	Huda, A. S. Nazmul
author_facet	Huda, A. S. Nazmul
author_sort	Huda, A. S. Nazmul
title	Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography
title_short	Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography
title_full	Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography
title_fullStr	Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography
title_full_unstemmed	Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography
title_sort	intelligent thermal condition monitoring of electrical equipment using infrared thermography
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2013
url	http://eprints.usm.my/46367/1/A.%20S.%20Nazmul%20Huda24.pdf
_version_	1747821658061668352

Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography

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