Eddy Current Inversion Models for Estimating Dimensions of Defects in Multilayered Structures

Siqi Li,Jun Dong,Gefei Qiu,Bo Ye,Biao Bai,Zhangzhou He,Tanghua Yang,Fang Zeng

doi:10.1155/2014/649608

Abstract

In eddy current nondestructive evaluation, one of the principal challenges is to determine the dimensions of defects in multilayered structures from the measured signals. It is a typical inverse problem which is generally considered to be nonlinear and ill-posed. In the paper, two effective approaches have been proposed to estimate the defect dimensions. The first one is a partial least squares (PLS) regression method. The second one is a kernel partial least squares (KPLS) regression method. The experimental research is carried out. In experiments, the eddy current signals responding to magnetic field changes are detected by a giant magnetoresistive (GMR) sensor and preprocessed for noise elimination using a wavelet packet analysis (WPA) method. Then, the proposed two approaches are used to construct the inversion models of defect dimension estimation. Finally, the estimation results are analyzed. The performance comparison between the proposed two approaches and the artificial neural network (ANN) method is presented. The comparison results demonstrate the feasibility and validity of the proposed two methods. Between them, the KPLS regression method gives a better prediction performance than the PLS regression method at present.

Highlights

Estimating dimensions of defects occurring in multilayered structures is important for ensuring the safety of the structural system, and for getting a huge economic benefit from the view of the possible extension of in-service inspection of period [1,2,3]
The problem of defect dimension estimation is formulated as an optimization problem, which seeks a set of defect dimensions by minimizing an objective function, representing the difference between the model predicted signals and the measured signals
The automatic system based on Eddy current nondestructive evaluation (ECNDE) for estimating dimensions of defects in multilayered structures is obtained by integrating the test device with a computer

Summary

Introduction

Estimating dimensions of defects occurring in multilayered structures is important for ensuring the safety of the structural system (e.g., aging nuclear structures, composite aircraft structures, and other civil engineering structures), and for getting a huge economic benefit from the view of the possible extension of in-service inspection of period [1,2,3]. Bernieri et al propose a model-free method for the reliable estimation of crack shape and dimensions based on the integration of an EC instrument and a support vector machine (SVM) processing algorithm [19] Among these methods, ANN is an efficient nonlinear statistical data modeling tool, but it usually requires a number of prior knowledge, space limitations, and database of defect signals for neural network training. The multivariate linear regression method can establish a direct and compact model Such method often fails to arrive at a sufficient accurate estimation due to the natural nonlinearity of the magnetic field distribution in complex multilayered structures.

Problem Description

Signal Preprocessing

Defect Dimension Estimation Approaches

Experiments and Results

Conclusions