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Defect depth estimation in passive thermography using neural network paradigm

Defect depth estimation from passive thermography data based on neural network paradigm is proposed. Three parameters have been found to be related with depth of the defect. Therefore, these parameters: the maximum temperature over the defective area (T-max), the temperature on the non-defective or...

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Main Authors: Heriansyah, Rudi, Syed Abu Bakar, Syed Abdul Rahman
Format: Conference or Workshop Item
Published: 2007
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/8628/
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spelling my.utm.86282012-03-08T06:34:51Z http://eprints.utm.my/id/eprint/8628/ Defect depth estimation in passive thermography using neural network paradigm Heriansyah, Rudi Syed Abu Bakar, Syed Abdul Rahman TK Electrical engineering. Electronics Nuclear engineering Defect depth estimation from passive thermography data based on neural network paradigm is proposed. Three parameters have been found to be related with depth of the defect. Therefore, these parameters: the maximum temperature over the defective area (T-max), the temperature on the non-defective or sound area (T-so), and the average temperature (T-avg) of the inspected area have been used as input parameters to train multilayer perceptron neural networks. For verification of the proposed scheme, NN has been tested with trained and untrained data. The correct depth estimation is 100% for trained data and more than 98% for untrained data. The result shows a great potential of the proposed method for defect depth estimation by means of passive thermography. 2007 Conference or Workshop Item PeerReviewed Heriansyah, Rudi and Syed Abu Bakar, Syed Abdul Rahman (2007) Defect depth estimation in passive thermography using neural network paradigm. In: 6th WSEAS International Conference on Circuits, Systems, Electrnics, Control & Signal Processing (CSECS'07).
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 TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Heriansyah, Rudi
Syed Abu Bakar, Syed Abdul Rahman
Defect depth estimation in passive thermography using neural network paradigm
description Defect depth estimation from passive thermography data based on neural network paradigm is proposed. Three parameters have been found to be related with depth of the defect. Therefore, these parameters: the maximum temperature over the defective area (T-max), the temperature on the non-defective or sound area (T-so), and the average temperature (T-avg) of the inspected area have been used as input parameters to train multilayer perceptron neural networks. For verification of the proposed scheme, NN has been tested with trained and untrained data. The correct depth estimation is 100% for trained data and more than 98% for untrained data. The result shows a great potential of the proposed method for defect depth estimation by means of passive thermography.
format Conference or Workshop Item
author Heriansyah, Rudi
Syed Abu Bakar, Syed Abdul Rahman
author_facet Heriansyah, Rudi
Syed Abu Bakar, Syed Abdul Rahman
author_sort Heriansyah, Rudi
title Defect depth estimation in passive thermography using neural network paradigm
title_short Defect depth estimation in passive thermography using neural network paradigm
title_full Defect depth estimation in passive thermography using neural network paradigm
title_fullStr Defect depth estimation in passive thermography using neural network paradigm
title_full_unstemmed Defect depth estimation in passive thermography using neural network paradigm
title_sort defect depth estimation in passive thermography using neural network paradigm
publishDate 2007
url http://eprints.utm.my/id/eprint/8628/
_version_ 1643645031729332224
score 13.211869

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