Space mapping methodology for defect recognition in eddy current testing – type NDT

Abstract

Purpose -The purpose of this paper is to solve an inverse problem of structure recognition arising in eddy current testing (ECT) -type NDT. For this purpose, the space mapping (SM) technique with an extraction based on the Gauss-Newton algorithm with Tikhonov regularization is applied. Design/methodology/approach -The aim is to have a computationally fast recognition procedure of defects since the monitoring results in a large amount of data points that need to be analyzed by 3D eddy current model. According to the SM optimization, the finite element method (FEM) is used as a fine model, while the model based on an integral method such as the volume integral method (VIM) serves as a coarse model. This approach, being an example of a two-level optimization method, allows shifting the optimization load from a time consuming and accurate model to the less precise but faster coarse surrogate. Findings -The application of this method enables shortening of the evaluation time that is required to provide the proper parameter estimation of surface defects. Research limitations/implications -In this work only the specific kinds of surface defects were considered. Therefore, the reconstruction of arbitrary shapes of defects when using real measurement data from ECT system can be treated in further research. Originality/value -The paper investigated the eddy current inverse problem. According to aggressive space mapping method, a suitable coarse model is needed. In this case, for the purpose of 3D defect reconstruction, the reduced VIM approach was applied. From a practical view point, the authors demonstrated that the two-level inversion procedures allow saving of up to 50 percent CPU time in comparison with the optimization by means of regularized Gauss-Newton algorithm in the same FE model.

Nanoelectronic COupled Problems Solutions: uncertainty quantification for analysis and optimization of an RFIC interference problem

“…Another effective solution relies in incorporating the UQ into the regularized Gauss-Newton procedure in order to find automatically impedances z. This approach applied in our work allows to reduce the coupling effect of y 3 (f ) in the mean sense approximately by 27 [dB] across the considered range of frequency ( Fig. 9).…”

“…The algorithm for the robust optimization has been implemented in Octave v.4.0.0 [9] using a scripting language. Thus, the least squares nonlinear optimization problem (15a)-(15c) has been solved in every iteration using the normal equation method and the Tikhonov regularization [27]. The final result of the robust optimization has been presented in Fig.…”

“…The proposed approach is innovative, since statistics based on uncertainty quantification (UQ) allows for assessing the most influential input random parameter, which can be further used for the physically-based design of RFIC model. Additionally, the UQ can be incorporated in the Gauss-Newton optimization algorithm [27], which yields the powerful tools for the automatic design of RFIC system, while taking both noise and uncertain input parameters into account. More specifically, based on results achieved in [28], the so-called mean gradient sensitivity analysis has been explored to approximate the derivative of the robust functional, which furthermore, has been applied in the optimization process.…”

Two-level approach for solving the inverse problem of defects identification in Eddy Current Testing - type NDT

“…Space Mapping (SM) is a highly recognized, efficient optimization method that has found a broad application to solve a wide range of engineering problems arising in various industry fields [10][11][12][13][29][30][31]. The main concept behind SM is to replace the traditional direct optimization procedure based on the accurate analysis of a computationally slow fine model with an iterative optimization and updating of a coarse model, which therefore, is cheaper to evaluate but also less precise.…”