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.

An improved PSO-SVM model for online recognition defects in eddy current testing

2013 ◽  
Vol 28 (4) ◽  
pp. 367-385 ◽  
Author(s):  
Baoling Liu ◽  
Dibo Hou ◽  
Pingjie Huang ◽  
Banteng Liu ◽  
Huayi Tang ◽  
...  
Keyword(s):  
Eddy Current ◽  
Eddy Current Testing ◽  
Current Testing ◽  
Online Recognition ◽  
Svm Model ◽  
Improved Pso

Novel austenitic steel ageing classification method using eddy current testing and a support vector machine

Measurement ◽  
2018 ◽  
Vol 127 ◽  
pp. 98-103 ◽  
Author(s):  
Mónica P. Arenas ◽  
Tiago J. Rocha ◽  
Chandra S. Angani ◽  
Artur L. Ribeiro ◽  
Helena G. Ramos ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Austenitic Steel ◽  
Eddy Current ◽  
Eddy Current Testing ◽  
Classification Method ◽  
Support Vector ◽  
Current Testing

Optimized Solutions for Defect Characterization in 2-D Inverse Eddy Current Testing Problems Using Subregion Finite Element Method

2018 ◽  
Vol 54 (8) ◽  
pp. 1-15 ◽  
Author(s):  
Mohammad R. Rawashdeh ◽  
Anders Rosell ◽  
Lalita Udpa ◽  
Samuel Ratnajeevan H. Hoole ◽  
Yiming Deng
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Eddy Current ◽  
Eddy Current Testing ◽  
Defect Characterization ◽  
Current Testing ◽  
Element Method

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.

Sizing of natural crack using multi-output support vector regression method from multi-frequency eddy current testing signals

2020 ◽  
Vol 64 (1-4) ◽  
pp. 721-728
Author(s):  
Li Wang ◽  
Zhenmao Chen
Keyword(s):  
Support Vector Regression ◽  
Quantitative Evaluation ◽  
Eddy Current ◽  
Crack Depth ◽  
Eddy Current Testing ◽  
Support Vector ◽  
Efficient Operation ◽  
Current Testing ◽  
Multiple Parameters ◽  
Equipment Sizing

In the nondestructive evaluation for components of key equipment, sizing of natural crack is important in order to guarantee both the safety and efficient operation for large mechanical systems. Natural cracks have complex boundary and there may be electric current flowing through crack faces. If a simple model of artificial notch is used to simulate it, errors often occur in crack depth reconstruction from eddy current testing (ECT) signals. However, if a complex crack conductivity model is used, quantitative evaluation of natural crack will be transformed into a multivariable nonlinear optimization problem and the solution is difficult. In this paper, based on the relationship between crack parameters and features of multi-frequency ECT signals, a multi-output support vector regression algorithm using domain decomposition for parameters was proposed. The algorithm realized the quantitative evaluation of multiple parameters of crack in turn. Numerical examples with simulated and measured ECT signals were presented to verify the efficiency of the proposed strategy.

Lorentz force eddy current testing: a novel NDE-technique

2014 ◽  
Vol 33 (6) ◽  
pp. 1965-1977 ◽  
Author(s):  
Hartmut Brauer ◽  
Konstantin Porzig ◽  
Judith Mengelkamp ◽  
Matthias Carlstedt ◽  
Marek Ziolkowski ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Lorentz Force ◽  
Eddy Current ◽  
Permanent Magnets ◽  
Measurement Data ◽  
Skin Depth ◽  
Eddy Current Testing ◽  
Experimental Realization ◽  
Content Type ◽  
Current Testing

Purpose – The purpose of this paper is to present a novel electromagnetic non-destructive evaluation technique, so called Lorentz force eddy current testing (LET). This method can be applied for the detection and reconstruction of defects lying deep inside a non-magnetic conducting material. Design/methodology/approach – In this paper the technique is described in general as well as its experimental realization. Besides that, numerical simulations are performed and compared to experimental data. Using the output data of measurements and simulations, an inverse calculation is performed in order to reconstruct the geometry of a defect by means of sophisticated optimization algorithms. Findings – The results show that measurement data and numerical simulations are in a good agreement. The applied inverse calculation methods allow to reconstruct the dimensions of the defect in a suitable accuracy. Originality/value – LET overcomes the frequency dependent skin-depth of traditional eddy current testing due to the use of permanent magnets and low to moderate magnetic Reynolds numbers (0.1-1). This facilitates the possibility to detect subsurface defects in conductive materials.

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