Kucheryavskiy Sergey, Egorov Alexander, Polyakov Victor
Department of Chemistry and Bioscience, Aalborg University, Niels Bohrs vej 8, 6700 Esbjerg, Denmark.
Department of Physics, Altai State University, Lenina str. 61, Barnaul 656049, Russia.
Sensors (Basel). 2021 Jan 17;21(2):618. doi: 10.3390/s21020618.
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.
涡流（EC）测量广泛用于导电材料的诊断，它高度依赖于样品的物理特性、几何形状以及EC传感器的设计。对于给定设计的传感器，样品的电导率、厚度以及样品与传感器之间的间隙（提离）是最具影响的参数。当所有这三个参数都未知且可能变化时，基于从传感器获取的信号来估计这些参数相当复杂。在本文中，我们提出了一种基于机器学习的方法来解决这个问题。该方法可以避免耗时且耗费资源的计算，并且不需要实验数据来训练预测模型。我们使用来自模拟和实际实验数据的独立测量集对该方法进行了测试。
