A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis

A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis

Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any def...

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Main Authors: A. S. N., Huda, S., Taib, Kamarul Hawari, Ghazali, M. S., Jadin
Format: Article
Language:English
Published: Elsevier 2014
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/5352/
http://umpir.ump.edu.my/id/eprint/5352/
http://umpir.ump.edu.my/id/eprint/5352/
http://umpir.ump.edu.my/id/eprint/5352/1/fkee-2014-08.pdf
id ump-5352
recordtype eprints
spelling ump-53522018-07-25T07:42:42Z http://umpir.ump.edu.my/id/eprint/5352/ A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis A. S. N., Huda S., Taib Kamarul Hawari, Ghazali M. S., Jadin TK Electrical engineering. Electronics Nuclear engineering Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg–Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively. Elsevier 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5352/1/fkee-2014-08.pdf A. S. N., Huda and S., Taib and Kamarul Hawari, Ghazali and M. S., Jadin (2014) A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis. ISA Transactions, 53 (3). pp. 717-724. ISSN 0019-0578 http://dx.doi.org/10.1016/j.isatra.2014.02.003 DOI: 10.1016/j.isatra.2014.02.003
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
A. S. N., Huda
S., Taib
Kamarul Hawari, Ghazali
M. S., Jadin
A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis
description Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg–Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.
format Article
author A. S. N., Huda
S., Taib
Kamarul Hawari, Ghazali
M. S., Jadin
author_facet A. S. N., Huda
S., Taib
Kamarul Hawari, Ghazali
M. S., Jadin
author_sort A. S. N., Huda
title A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis
title_short A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis
title_full A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis
title_fullStr A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis
title_full_unstemmed A New Thermographic NDT for Condition Monitoring of Electrical Components Using ANN with Confidence Level Analysis
title_sort new thermographic ndt for condition monitoring of electrical components using ann with confidence level analysis
publisher Elsevier
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/5352/
http://umpir.ump.edu.my/id/eprint/5352/
http://umpir.ump.edu.my/id/eprint/5352/
http://umpir.ump.edu.my/id/eprint/5352/1/fkee-2014-08.pdf
first_indexed 2023-09-18T22:00:40Z
last_indexed 2023-09-18T22:00:40Z
_version_ 1777414376146337792

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