Decoupling Permeability, Conductivity, Thickness, Lift-Off for Eddy Current Testing Using Machine Learning
Pu Huang, Zhenyu Bao, Ruochen Huang, Jieshu Jia, Kuanyao Liu, Yu Xing, Wuliang Yin, Yuedong Xie
Abstract
This paper 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. The whole processing mainly divided into characteristic extractions and data driven machine learning model. According to the classical Dodd and Deeds analytical solution, the characteristic value of mutual inductance signal related with metallic plates parameters are 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 only using characteristic information. In this paper, the machine learning modelling combining the extracted features is established to achieve decoupling and high precision inversion. The data required for model training comes from the customized fast FEM solver and analytical solution. While, the experiments data acts 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%.