Metallic plates are widely used in a variety of industrial applications, from which conductivity is a key parameter related to the quality of metals. In order to accurately measure the conductivity, this article proposes a novel eddy current method based on sweep-frequency strategy. First, a simplified theoretical model for conductivity measurement is derived from the classical Dodd and Deeds analytical solution. Based on the analytical solution, the real and imaginary parts of the mutual inductance signal have an intersection in the frequency domain. According to mathematical derivation, the corresponding frequency (crossover frequency) of the intersection is inversely proportional to the conductivity of the sample. Moreover, the liftoff compensation method is proposed to reduce the effect of liftoff variation. Experiments have been carried out for samples with different conductivities, ranging from 0.5 to 58 MS/m, to verify the proposed method. The results show that the average error of the conductivity estimation with the proposed approach can be controlled within 1.1%, and the liftoff compensation method can significantly reduce the conductivity estimation error.

Titanium and stainless steel are widely used in industries due to their advantages of high strength and good corrosion resistance. Such materials have common electromagnetic features – with a relatively low conductivity ～10⁵ − 10⁶ S/m and a relative permeability of 1. In terms of structure health monitoring, conductivity is an important physical parameter of metal materials, which is closely related to its purity, heat treatment state, internal stress state, hardness and temperature. Therefore, accurate and efficient conductivity estimation of such metals is extremely important to ensure product quality and normal operation of equipment. This paper presents an efficient conductivity estimation method of metals based on eddy current testing. The sensors used are with two coaxially cylindrical coils acting as transmitting coil and receiving coil respectively. According to the characteristics of low-conductivity and sensor configuration, a simplified theoretical model derived from the classic Dodd–Deeds theory is proposed. Moreover, the finite element method (FEM) simulation and experiments are carried out to verify the proposed model. The experiments are carried out on stainless steel 316 and different Titanium alloys with a commercial impedance analyzer, and the results indicate that the average relative measurement error is less than 2.8%.

Eddy current (EC) sensors have been applied to detect defects for many years. Since both defects and the lift-off variation will lead to measurement signal fluctuation, it is necessary to distinguish the defect response and lift-off variation response. In this work, a novel EC method is proposed for defect detection immune to lift-off via a triple-coil structure. Firstly, a simplified theoretical model is built to investigate the relationship between the lift-off and the inductance variation. It is found the lift-off is proportional to the logarithm of inductance variation in real part under high frequency. In terms of sensor design, the triple-coil sensor consists of two pairs of transmitting–receiving coils, from which one of the receiving coils is larger than the other one. With such sensor structure, the small-size coil aims to detect the defect, while the large-size coil aims to calibrate lift-off. The proposed method is verified by experiments with the sensor scanning over a Titanium specimen with defects of different sizes. The results indicate the proposed method can achieve efficient and precise defect detection immune to lift-off.

This paper presents a review on the biomedical applications of electromagnetic detection in recent years. First of all, the thermal, non-thermal, and cumulative thermal effects of electromagnetic field on organism and their biological mechanisms are introduced. According to the electromagnetic biological theory, the main parameters affecting electromagnetic biological effects are frequency and intensity. This review subsequently makes a brief review about the related biomedical application of electromagnetic detection and biosensors using frequency as a clue, such as health monitoring, food preservation, and disease treatment. In addition, electromagnetic detection in combination with machine learning (ML) technology has been used in clinical diagnosis because of its powerful feature extraction capabilities. Therefore, the relevant research involving the application of ML technology to electromagnetic medical images are summarized. Finally, the future development to electromagnetic detection for biomedical applications are presented.

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%.