Measurements of near-surface radial profiles of electrophysical characteristics of cylindrical objects by the eddy current method using a priori data

IF 0.1 Q4 INSTRUMENTS & INSTRUMENTATION
V. Halchenko, A. Storchak, V. Tychkov, R. Trembovetska
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引用次数: 0

Abstract

A new multiparameter express method for eddy-current measurement of radial near-surface profiles of electrophysical parameters of cylindrical control objects with a priori accumulation of information about them is proposed. The method combines in-situ measurements and model calculations using high-performance computing technologies of artificial intelligence based on neural networks, carried out both in advance in order to obtain specific information about objects, and directly in the process of performing measurements to quickly obtain a result. Mathematically, the method is based on the unique ability to quickly solve Maxwell's equations as a result of its approximation by deep neural networks without actually explicitly executing this solution. This allows deep learning to be used not only in the forward direction, but also in the opposite direction, i.e. apply to solve inverse measuring problems. The method is universal and can be extended to multiparameter measurement control with simultaneous additional determination of the diameter of a cylindrical object. The adequacy of the proposed method by numerical experiments is proved; examples of the implementation of all stages of its application are given. Algorithms and a complex of programs in the Python 3 environment have been created, which make it possible to practically implement the method. The profile measurement accuracy established on model calculations is characterized by maximum relative errors not exceeding 0.5%, provided that the probe signal is perfectly fixed. It is possible to generalize the use of the proposed method to similar eddy current measurements with surface probes of profiles of material parameters of flat objects.
利用先验数据涡流法测量圆柱形物体电物理特性的近表面径向分布
提出了一种新的多参数表达方法,用于涡流测量圆柱形控制对象的电物理参数的径向近表面轮廓,并先验地积累有关它们的信息。该方法使用基于神经网络的高性能人工智能计算技术,将现场测量和模型计算相结合,既可以提前进行以获得有关物体的特定信息,也可以直接在进行测量的过程中快速获得结果。在数学上,该方法基于快速求解麦克斯韦方程组的独特能力,这是通过深度神经网络进行近似的结果,而无需实际明确执行该解。这使得深度学习不仅可以正向使用,还可以反向使用,即应用于解决反向测量问题。该方法具有通用性,可扩展到多参数测量控制,同时额外确定圆柱形物体的直径。数值实验证明了该方法的充分性;文中给出了其应用各个阶段的实现实例。已经在Python3环境中创建了算法和复杂的程序,这使得实际实现该方法成为可能。在模型计算中建立的轮廓测量精度的特点是最大相对误差不超过0.5%,前提是探头信号完全固定。可以将所提出的方法推广到使用平面物体材料参数轮廓的表面探针进行类似的涡流测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ukrainian Metrological Journal
Ukrainian Metrological Journal INSTRUMENTS & INSTRUMENTATION-
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发文量
21
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