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Sensors 2016, 16(5), 649; doi:10.3390/s16050649

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

1
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, Xuzhou 221116, China
3
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
4
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
5
School of Electrical and Electronic Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas Hütten
Received: 7 February 2016 / Revised: 24 April 2016 / Accepted: 3 May 2016 / Published: 7 May 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3352 KB, uploaded 7 May 2016]   |  

Abstract

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. View Full-Text
Keywords: nondestructive testing; eddy current sensor; frequency optimization; probe response; feature extraction; defect classification nondestructive testing; eddy current sensor; frequency optimization; probe response; feature extraction; defect classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fan, M.; Wang, Q.; Cao, B.; Ye, B.; Sunny, A.I.; Tian, G. Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique. Sensors 2016, 16, 649.

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