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Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography
Journal article   Peer reviewed

Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography

Y. Chen, F. Sohel, S.A.A. Shah and S. Ding
Optik, Vol.219, Art. 164828
2020
DOI: https://doi.org/10.1016/j.ijleo.2020.164828
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Abstract

The aim of this paper is to classify conductive material corrosion by eddy current pulsed thermography. Thermal transient images generate a large of amount of data which is difficult for accurate detection and classification of the different corrosion materials, especially with the hidden corrosion. We apply deep Boltzmann machines (DBM) network to automatically extract and classify features from the whole measured area. Corrosion classification is tested with several different machine learning based algorithms including: clustering, PCA, multi-layer DBM classifier. The performance of the proposed framework is measured in terms of accuracy, sensitivity, specificity and precision. Several experiments are performed on a dataset of eddy current signal samples for four different corrosion degrees. The results show that our method outperforms the existing algorithms in classification accuracy (97.9%), sensitivity (96.1%), precision (97.1%), and especially specificity (98.4%).

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75 Record Views
7 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
7 Engineering & Materials Science
7.226 Electrical - Sensors & Monitoring
7.226.1316 Infrared Thermography
Web Of Science research areas
Optics
ESI research areas
Physics
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