Abstract
This paper describes a non-intrusive method for collecting data about internal corrosion damages in AISI-304 stainless steel plates and classifying them according to severity. The mapping of the electric potential gradient is derived using the potential drop technique, which is then analyzed using image processing techniques including edge enhancement and segmentation. Simulations were run using finite element modeling to produce examples of damaged plates, with four types of defects that can be considered part of pitting corrosion. The image processing stage plays the role of an extractor of features that, when employed as inputs of machine learning algorithms, make it possible to determine the damage severity. With the Gradient Boosting regressor, the maximum absolute error of 0.879 mm was obtained in the estimate of the depth of the defects. Additionally, with the application of a Convolutional Neural Network, an accuracy of 94.84% was achieved to classify of the severity of the damages.
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An early version of paper was presented at XXIII Congresso Brasileiro de Automática (CBA 2020). The authors would like to acknowledge the financial support provided by FAPERJ —Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro and CAPES—Finance Code 001.
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Pinto, G., Amaral, J., Pinheiro, G.R.V. et al. Non-intrusive Internal Corrosion Characterization using the Potential Drop Technique for Electrical Mapping and Machine Learning. J Control Autom Electr Syst 33, 183–197 (2022). https://doi.org/10.1007/s40313-021-00823-9
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DOI: https://doi.org/10.1007/s40313-021-00823-9