Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography

Yuming Chen, Ferdous Sohel, Syed Afaq Ali Shah, Song Ding

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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%).

Original languageEnglish
Article number164828
Publication statusPublished - Oct 2020


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