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Updated: Nov 20, 2025

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors
Published on: January 16, 2020
Coupling Analytical Models and Machine Learning Methods for Fast and Reliable Resolution of Effects in Multifrequency
Sergey Kucheryavskiy1, Alexander Egorov2, Victor Polyakov2,3
1Department of Chemistry and Bioscience, Aalborg University, Niels Bohrs vej 8, 6700 Esbjerg, Denmark.
Abstract:
Eddy current (EC) measurements, widely used for diagnostics of conductive materials, are highly dependent on physical properties and geometry of a sample as well as on a design of an EC-sensor. For a sensor of a given design, the conductivity and thickness of a sample as well as the gap between the sample and the sensor (lift-off) are the most influencing parameters. Estimation of these parameters, based on signals acquired from the sensor, is quite complicated in case when all three parameters are unknown and may vary. In this paper, we propose a machine learning based approach for solving this problem. The approach makes it possible to avoid time and resource-consuming computations and does not require experimental data for training of the prediction models. The approach was tested using independent sets of measurements from both simulated and real experimental data.
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