The inverse problem of determining profiles of electrophysical parameters in eddy-current structuroscopy using apriori information on multifrequency probing

• Published in: Mathematical modelling and analysis Vol. 29; no. 4; pp. 767 - 780
• Main Authors: Halchenko, Volodymyr Ya, Trembovetska, Ruslana, Tychkov, Volodymyr, Tychkova, Nataliia
• Format: Journal Article
• Language: English
• Published: Vilnius Vilnius Gediminas Technical University 29.11.2024
• Subjects: Accuracy; active subspace; Analysis; apriori information; Computer simulation; eddy current measurement control; Eddy current testing; Electric properties; Electrical conductivity; Electrical resistivity; Inverse problems; Investigations; Magnetic permeability; Measurement techniques; Metamodels; multifrequency probing; Neural networks; Optimization algorithms; Parameter identification; Permeability; profiles of magnetic permeability and electrical conductivity; Research methodology; surrogate optimization; eddy current measurement control; multifrequency probing; metamodel; surrogate optimization; apriori information; profiles of magnetic permeability and electrical conductivity; active subspace; deep neural networks
• ISSN: 1392-6292; 1648-3510; 1648-3510
• DOI: 10.3846/mma.2024.20022

Based on the proposed methodology, the essence of which is to identify the profiles of electrophysical parameters of planar objects of eddy-current testing by means of surrogate optimization in the active PCA-space of reduced dimensionality, the effectiveness of the approach is proved by modeling the process of measurement control using apriori accumulated information about an object, in particular, multifrequency probing. The particularity of these studies is the consideration of previously collected information not only on profile variations, but also on the effect of various object probing frequencies on the signal of the surface probe. The functions of the storage device and information carrier were performed by a neural network metamodel, characterized by a high computational efficiency. Numerical experiments have determined the accuracy indicators of the proposed improved method for determining the distributions of magnetic permeability and electrical conductivity along the subsurface layer of a metal object with changes in a microstructure. The analysis of the modeling results indicates a significant reduction in the level of computational resources required to solve the problem and an increase in the accuracy of profile identification.