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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MDPI AG
2016-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/5/649 |
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| author | Mengbao Fan Qi Wang Binghua Cao Bo Ye Ali Imam Sunny Guiyun Tian |
| author_facet | Mengbao Fan Qi Wang Binghua Cao Bo Ye Ali Imam Sunny Guiyun Tian |
| author_sort | Mengbao Fan |
| collection | DOAJ |
| 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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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:12:43Z |
| publishDate | 2016-05-01 |
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| spelling | doaj.art-dcc25af5e70a4fe28c44f80214c6491a2022-12-22T02:52:51ZengMDPI AGSensors1424-82202016-05-0116564910.3390/s16050649s16050649Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current TechniqueMengbao Fan0Qi Wang1Binghua Cao2Bo Ye3Ali Imam Sunny4Guiyun Tian5School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Electrical and Electronic Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UKSchool of Electrical and Electronic Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UKEddy 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.http://www.mdpi.com/1424-8220/16/5/649nondestructive testingeddy current sensorfrequency optimizationprobe responsefeature extractiondefect classification |
| spellingShingle | Mengbao Fan Qi Wang Binghua Cao Bo Ye Ali Imam Sunny Guiyun Tian Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique Sensors nondestructive testing eddy current sensor frequency optimization probe response feature extraction defect classification |
| 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 | nondestructive testing eddy current sensor frequency optimization probe response feature extraction defect classification |
| url | http://www.mdpi.com/1424-8220/16/5/649 |
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