Decoupling Permeability, Conductivity, Thickness, Lift-Off for Eddy Current Testing Using Machine Learning

This article proposed a method to simultaneously estimate the permeability, conductivity, thickness, and lift-off of metallic plates using sweep frequency eddy current measurement combined with machine learning (ML). The whole processing was mainly divided into characteristic extractions and a data-driven machine learning model. According to the classical Dodd and Deeds analytical solution, the characteristic value of mutual inductance signal related to metallic plate parameters is extracted based on theoretical derivation. The parameters of metallic plates are inherently coupled together; thus, the parameters of metallic plates cannot be directly and accurately obtained using only characteristic information. In this article, the machine learning modeling combining the extracted features is established to achieve decoupling and high-precision inversion. The data required for model training comes from the customized fast finite element method (FEM) solver and analytical solution. While the experiment data act as the test set to verify the performance of the proposed method. The results show the proposed method can be applied to accurately estimate the parameters of metallic plates, and the relative errors of inverted parameters are within 3.5%.