Eddy current characterization of small cracks using least square support vector machine

Eddy current (EC) sensors are used for non-destructive testing since they are able to probe conductive materials. Despite being a conventional technique for defect detection and localization, the main weakness of this technique is that defect characterization, of the exact determination of the shape...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. D, Applied physics Applied physics, 2016-03, Vol.49 (15), p.155303-155309
Main Authors: Chelabi, M, Hacib, T, Le Bihan, Y, Ikhlef, N, Boughedda, H, Mekideche, M R
Format: Article
Language:English
Subjects:
Computer simulation
Cracks
database adapter
Defects
eddy current sensor
Engineering Sciences
finite element method
genetic algorithm
Genetic algorithms
inverse problem
least square support vector machine
Mathematical analysis
particle swarm optimization
Sensors
Support vector machines
Swarm intelligence
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Eddy current (EC) sensors are used for non-destructive testing since they are able to probe conductive materials. Despite being a conventional technique for defect detection and localization, the main weakness of this technique is that defect characterization, of the exact determination of the shape and dimension, is still a question to be answered. In this work, we demonstrate the capability of small crack sizing using signals acquired from an EC sensor. We report our effort to develop a systematic approach to estimate the size of rectangular and thin defects (length and depth) in a conductive plate. The achieved approach by the novel combination of a finite element method (FEM) with a statistical learning method is called least square support vector machines (LS-SVM). First, we use the FEM to design the forward problem. Next, an algorithm is used to find an adaptive database. Finally, the LS-SVM is used to solve the inverse problems, creating polynomial functions able to approximate the correlation between the crack dimension and the signal picked up from the EC sensor. Several methods are used to find the parameters of the LS-SVM. In this study, the particle swarm optimization (PSO) and genetic algorithm (GA) are proposed for tuning the LS-SVM. The results of the design and the inversions were compared to both simulated and experimental data, with accuracy experimentally verified. These suggested results prove the applicability of the presented approach.
ISSN:0022-3727
1361-6463
DOI:10.1088/0022-3727/49/15/155303