Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier
Magnetic flux leakage (MFL) detection is commonly employed to detect wire rope defects. However, nondestructive testing (NDT) of a wire rope still has problems. A wire rope nondestructive testing device based on a double detection board is designed to solve the problems of large volume, complex oper...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/1683494 |
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| author | Jigang Li Juwei Zhang |
| author_facet | Jigang Li Juwei Zhang |
| author_sort | Jigang Li |
| collection | DOAJ |
| description | Magnetic flux leakage (MFL) detection is commonly employed to detect wire rope defects. However, nondestructive testing (NDT) of a wire rope still has problems. A wire rope nondestructive testing device based on a double detection board is designed to solve the problems of large volume, complex operations, and limited circumferential resolution due to sensor size in traditional devices. The device adopts two magnetic sensor arrays to form the double detection board and collects the MFL data of the magnetized wire rope. These sensors on the double detection board are staggered and evenly arranged on the circumference of the wire rope. A super-resolution algorithm based on interpolation uses non-subsampled shearlet transform (NSST) combining principal component analysis (PCA) and Gaussian fuzzy logic (GFL) and fuses the data of double detection board to improve the resolution and quality of defect images. Image quality measurement and comparison experiments are designed to verify that defect images are effectively enhanced. An AdaBoost classifier is designed to classify defects by texture features and invariant moments of defect images. The experimental results show that the detection device not only improves the circumferential resolution, but also the operation is simple; the resolution and quality of the defect images are improved by the proposed super-resolution algorithm, and defects are identified by using the AdaBoost classifier. |
| format | Article |
| id | doaj-art-705eff50872f4db7b6b8d3bb6f5d26e4 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-705eff50872f4db7b6b8d3bb6f5d26e42025-02-03T01:20:34ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/16834941683494Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost ClassifierJigang Li0Juwei Zhang1College of Electrical Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Electrical Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaMagnetic flux leakage (MFL) detection is commonly employed to detect wire rope defects. However, nondestructive testing (NDT) of a wire rope still has problems. A wire rope nondestructive testing device based on a double detection board is designed to solve the problems of large volume, complex operations, and limited circumferential resolution due to sensor size in traditional devices. The device adopts two magnetic sensor arrays to form the double detection board and collects the MFL data of the magnetized wire rope. These sensors on the double detection board are staggered and evenly arranged on the circumference of the wire rope. A super-resolution algorithm based on interpolation uses non-subsampled shearlet transform (NSST) combining principal component analysis (PCA) and Gaussian fuzzy logic (GFL) and fuses the data of double detection board to improve the resolution and quality of defect images. Image quality measurement and comparison experiments are designed to verify that defect images are effectively enhanced. An AdaBoost classifier is designed to classify defects by texture features and invariant moments of defect images. The experimental results show that the detection device not only improves the circumferential resolution, but also the operation is simple; the resolution and quality of the defect images are improved by the proposed super-resolution algorithm, and defects are identified by using the AdaBoost classifier.http://dx.doi.org/10.1155/2019/1683494 |
| spellingShingle | Jigang Li Juwei Zhang Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier Shock and Vibration |
| title | Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier |
| title_full | Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier |
| title_fullStr | Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier |
| title_full_unstemmed | Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier |
| title_short | Quantitative Nondestructive Testing of Wire Rope Using Image Super-Resolution Method and AdaBoost Classifier |
| title_sort | quantitative nondestructive testing of wire rope using image super resolution method and adaboost classifier |
| url | http://dx.doi.org/10.1155/2019/1683494 |
| work_keys_str_mv | AT jigangli quantitativenondestructivetestingofwireropeusingimagesuperresolutionmethodandadaboostclassifier AT juweizhang quantitativenondestructivetestingofwireropeusingimagesuperresolutionmethodandadaboostclassifier |