Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks

2013 ◽  
Vol 62 (5) ◽  
pp. 1207-1214 ◽  
Author(s):  
Luis S. Rosado ◽  
Fernando M. Janeiro ◽  
Pedro M. Ramos ◽  
Moises Piedade
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Nonlinear Regression ◽  
Eddy Current ◽  
Eddy Current Testing ◽  
Defect Characterization ◽  
Current Testing ◽  
Artificial Neural

Application of back-propagation neural networks to defect characterization using eddy current testing

2020 ◽  
Vol 64 (1-4) ◽  
pp. 817-825
Author(s):  
Xinwu Zhou ◽  
Ryoichi Urayama ◽  
Tetsuya Uchimoto ◽  
Toshiyuki Takagi
Keyword(s):  
Neural Networks ◽  
Eddy Current ◽  
Back Propagation ◽  
Eddy Current Testing ◽  
Defect Characterization ◽  
Current Testing ◽  
Conductive Materials ◽  
Back Propagation Neural Networks ◽  
Lift Off ◽  
Relative Errors

Eddy current testing is widely used for the automatic detection of defects in conductive materials. However, this method is strongly affected by probe scanning conditions and requires signal analysis to be carried out by experienced inspectors. In this study, back-propagation neural networks were used to predict the depth and length of unknown slits by analyzing eddy current signals in the presence of noise caused by probe lift-off and tilting. The constructed neural networks were shown to predict the depth and length of defects with relative errors of 4.6% and 6.2%, respectively.

Impedance inversion in eddy current testing of layered planar structures via neural networks

1997 ◽  
Vol 30 (2) ◽  
pp. 69-74 ◽  
Author(s):  
I.T. Rekanos ◽  
T.P. Theodoulidis ◽  
S.M. Panas ◽  
T.D. Tsiboukis
Keyword(s):  
Neural Networks ◽  
Eddy Current ◽  
Eddy Current Testing ◽  
Current Testing ◽  
Planar Structures ◽  
Impedance Inversion

The combination of adaptive database SDM and multi-output SVM for eddy current testing

2015 ◽  
Vol 34 (6) ◽  
pp. 1731-1739 ◽  
Author(s):  
M. Chelabi ◽  
T. Hacib ◽  
Z. Belli ◽  
M. R. Mekideche ◽  
Y. Le Bihan
Keyword(s):  
Eddy Current ◽  
Eddy Current Testing ◽  
Learning Performance ◽  
Support Vector ◽  
Inversion Method ◽  
Defect Characterization ◽  
Content Type ◽  
Current Testing ◽  
Svm Model ◽  
Schema Design

Purpose – Eddy current testing (ECT) is a nondestructive testing method for the detection of flaws that uses electromagnetic induction to find defects in conductive materials. In this method, eddy currents are generated in a conductive material by a changing magnetic field. A defect is detected when there is a disruption in the flow of the eddy current. The purpose of this paper is to develop a new noniterative inversion methodology for detecting degradation (defect characterization) such as cracking, corrosion and erosion from the measurement of the impedance variations. Design/methodology/approach – The methodology is based on multi-output support vector machines (SVM) combined with the adaptive database schema design method (SDM). The forward problem was solved numerically using finite element method (FEM), with its accuracy experimentally verified. The multi-output SVM is a statistical learning method that has good generalization capability and learning performance. FEM is used to create the adaptive database required to train the multi-output SVM and the genetic algorithm is used to tune the parameters of multi-output SVM model. Findings – The results show the applicability of multi-output SVM to solve eddy current inverse problems instead of using traditional iterative inversion methods which can be very time-consuming. With the experimental results the authors demonstrate the accuracy which can be provided by the multi-output SVM technique. Practical implications – The work allows extending the capability of the experimentation ECT defect characterization system developed at LGEP. Originality/value – A new inversion method is developed and applied to ECT defect characterization. This new concept introduces multi-output SVM in the context of ECT. The real data together with estimated one obtained by multi-output SVM model are compared in order to evaluate the effectiveness of the developed technique.

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