Characterization of Wire Rope Defects with Gray Level Co-occurrence Matrix of Magnetic Flux Leakage Images
Magnetic flux leakage (MFL) techniques are used extensively for non-intrusively detecting and characterizing wire rope defects. Traditionally, MFL signals are captured with induction coil sensors. However, the output of coil sensors is related to the wire rope speed, and they can only provide the ax...
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Published in | Journal of nondestructive evaluation Vol. 32; no. 1; pp. 37 - 43 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Springer-Verlag
01.03.2013
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Abstract | Magnetic flux leakage (MFL) techniques are used extensively for non-intrusively detecting and characterizing wire rope defects. Traditionally, MFL signals are captured with induction coil sensors. However, the output of coil sensors is related to the wire rope speed, and they can only provide the axial distribution along the wire rope. Hall sensors array are designed due to the limitation of coil sensors. In this paper, a Hall sensors array was designed to capture the MFL signals both axially and circumferentially. 30-channel data from Hall sensors are processed to compose a MFL image. A digital image process technique is introduced to preprocess the MFL image, the MFL images from different types of defects show different texture characteristics. Gray level co-occurrence matrix is utilized for feature extraction of the texture in the MFL image. Five typical eigenvalues (contrast, correlation, energy, homogeneity and entropy) are used as the inputs of back propagation (BP) networks. After training with typical samples, the BP networks show good performance in the quantitative recognition of different defects. The result of this work shows that texture analysis method for MFL image is suitable for feature extraction and quantitative detection of wire rope defects. |
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AbstractList | Magnetic flux leakage (MFL) techniques are used extensively for non-intrusively detecting and characterizing wire rope defects. Traditionally, MFL signals are captured with induction coil sensors. However, the output of coil sensors is related to the wire rope speed, and they can only provide the axial distribution along the wire rope. Hall sensors array are designed due to the limitation of coil sensors. In this paper, a Hall sensors array was designed to capture the MFL signals both axially and circumferentially. 30-channel data from Hall sensors are processed to compose a MFL image. A digital image process technique is introduced to preprocess the MFL image, the MFL images from different types of defects show different texture characteristics. Gray level co-occurrence matrix is utilized for feature extraction of the texture in the MFL image. Five typical eigenvalues (contrast, correlation, energy, homogeneity and entropy) are used as the inputs of back propagation (BP) networks. After training with typical samples, the BP networks show good performance in the quantitative recognition of different defects. The result of this work shows that texture analysis method for MFL image is suitable for feature extraction and quantitative detection of wire rope defects. |
Author | Zhao, Min Pan, Shimin Zhou, Zhihui Zhang, Donglai |
Author_xml | – sequence: 1 givenname: Donglai surname: Zhang fullname: Zhang, Donglai email: zhangdonglai@263.net organization: Harbin Institute of Technology Shenzhen Graduate School – sequence: 2 givenname: Min surname: Zhao fullname: Zhao, Min organization: Harbin Institute of Technology Shenzhen Graduate School – sequence: 3 givenname: Zhihui surname: Zhou fullname: Zhou, Zhihui organization: Harbin Institute of Technology Shenzhen Graduate School – sequence: 4 givenname: Shimin surname: Pan fullname: Pan, Shimin organization: Harbin Institute of Technology Shenzhen Graduate School |
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Cites_doi | 10.1016/j.ndteint.2006.04.003 10.1016/j.ndteint.2006.09.004 10.1016/j.ndteint.2007.10.006 10.1016/S0921-4534(01)01142-X 10.1109/TMAG.2008.2011896 10.1109/TMAG.2002.804817 10.1016/S0963-8695(99)00011-0 10.1109/19.989914 10.1109/TSMC.1973.4309314 10.1016/j.ndteint.2011.08.011 10.1155/2012/129074 |
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Keywords | Wire rope defects Feature extraction Magnetic flux leakage image Gray level co-occurrence matrix |
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Snippet | Magnetic flux leakage (MFL) techniques are used extensively for non-intrusively detecting and characterizing wire rope defects. Traditionally, MFL signals are... |
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SubjectTerms | Back propagation Characterization and Evaluation of Materials Classical Mechanics Control Defects Dynamical Systems Engineering Entropy Sensors Solid Mechanics Surface layer Texture Vibration Wire rope |
Title | Characterization of Wire Rope Defects with Gray Level Co-occurrence Matrix of Magnetic Flux Leakage Images |
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