preprints.org > engineering > electrical and electronic engineering > doi: 10.20944/preprints202403.0575.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 9 March 2024 / Approved: 11 March 2024 / Online: 11 March 2024 (05:42:13 CET)

it still undergoes corrosion when exposed to corrosive environments. This paper proposes an evaluation method for assessing the corrosion level of SPCC steel specimens using traditional eddy current testing (ECT), along with two different machine learning approaches. The objective is to classify the corroded state of the specimens into two states: a less corroded state (state-1) and a highly corroded state (state-2). Generative and discriminative models were implemented for classification. The generative classifier was based on the Gaussian mixture model (GMM), while the discriminative model was based on the logistic regression model. The models used features based on the absolute maximum of the perturbation magnetic field components at two different frequencies. The performance of the classifiers was evaluated using metrics such as absolute error, accuracy, precision, recall, and F1 score. The results indicate that high classification accuracy can be achieved based on both methods using traditional eddy current testing.

classification; corrosion; generative and discriminative models; traditional eddy current testing

Engineering, Electrical and Electronic Engineering

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