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

Lookup NU author(s): Dr Mengbao Fan, Dr Binghua Cao, Ali Sunny, Professor Gui Yun Tian

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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.


Publication metadata

Author(s): Fan MB, Wang Q, Cao BH, Ye B, Sunny AI, Tian GY

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2016

Volume: 16

Issue: 5

Print publication date: 01/05/2016

Online publication date: 07/05/2016

Acceptance date: 03/05/2016

Date deposited: 13/12/2017

ISSN (electronic): 1424-8220

Publisher: MDPI AG

URL: http://dx.doi.org/10.3390/s16050649

DOI: 10.3390/s16050649


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