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Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealin...

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Autores principales: Fan, Mengbao, Wang, Qi, Cao, Binghua, Ye, Bo, Sunny, Ali Imam, Tian, Guiyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883340/
https://www.ncbi.nlm.nih.gov/pubmed/27164112
http://dx.doi.org/10.3390/s16050649
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author Fan, Mengbao
Wang, Qi
Cao, Binghua
Ye, Bo
Sunny, Ali Imam
Tian, Guiyun
author_facet Fan, Mengbao
Wang, Qi
Cao, Binghua
Ye, Bo
Sunny, Ali Imam
Tian, Guiyun
author_sort Fan, Mengbao
collection PubMed
description Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances.
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spelling pubmed-48833402016-05-27 Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique Fan, Mengbao Wang, Qi Cao, Binghua Ye, Bo Sunny, Ali Imam Tian, Guiyun Sensors (Basel) Article Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances. MDPI 2016-05-07 /pmc/articles/PMC4883340/ /pubmed/27164112 http://dx.doi.org/10.3390/s16050649 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fan, Mengbao
Wang, Qi
Cao, Binghua
Ye, Bo
Sunny, Ali Imam
Tian, Guiyun
Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique
title Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique
title_full Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique
title_fullStr Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique
title_full_unstemmed Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique
title_short Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique
title_sort frequency optimization for enhancement of surface defect classification using the eddy current technique
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883340/
https://www.ncbi.nlm.nih.gov/pubmed/27164112
http://dx.doi.org/10.3390/s16050649
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