Nondestructive Testing of Steel Wire Rope Based on Gagnetic Signal and Infrared Information
This paper designs a two-dimensional magnetic signal detection device under weak magnetic excitation, which solves the problem of large volume and single signal acquisition of traditional one-dimensional detection devices. To reduce the original noise, a noise reduction algorithm combining wavelet t...
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| Veröffentlicht in: | Russian journal of nondestructive testing 2023-09, Vol.59 (9), p.991-1004 |
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| container_title | Russian journal of nondestructive testing |
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| creator | Zhang, Juwei Chen, Quankun Ye, Qiang |
| description | This paper designs a two-dimensional magnetic signal detection device under weak magnetic excitation, which solves the problem of large volume and single signal acquisition of traditional one-dimensional detection devices. To reduce the original noise, a noise reduction algorithm combining wavelet transform and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed. A super-resolution fusion algorithm is proposed to fuse two-dimensional magnetic signals to achieve image enhancement. Finally, the convolutional neural network is used to extract the features of the two types of images, and then the features are fused, and the support vector machine (SVM) is used to classify. Under the condition of zero broken wire error, compared with the subjectively extracted color features and texture features of the two types of images as the SVM input, this algorithm’s recognition rate is increased by 37.26%. |
| doi_str_mv | 10.1134/S1061830923600399 |
| format | Article |
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To reduce the original noise, a noise reduction algorithm combining wavelet transform and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed. A super-resolution fusion algorithm is proposed to fuse two-dimensional magnetic signals to achieve image enhancement. Finally, the convolutional neural network is used to extract the features of the two types of images, and then the features are fused, and the support vector machine (SVM) is used to classify. Under the condition of zero broken wire error, compared with the subjectively extracted color features and texture features of the two types of images as the SVM input, this algorithm’s recognition rate is increased by 37.26%.</description><identifier>ISSN: 1061-8309</identifier><identifier>EISSN: 1608-3385</identifier><identifier>DOI: 10.1134/S1061830923600399</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Algorithms ; Artificial neural networks ; Characterization and Evaluation of Materials ; Chemistry and Materials Science ; Complex Methods of Control ; Image enhancement ; Magnetic signals ; Materials Science ; Noise reduction ; Nondestructive testing ; Signal detection ; Steel wire ; Structural Materials ; Support vector machines ; Wavelet transforms ; Wire rope</subject><ispartof>Russian journal of nondestructive testing, 2023-09, Vol.59 (9), p.991-1004</ispartof><rights>Pleiades Publishing, Ltd. 2023. 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To reduce the original noise, a noise reduction algorithm combining wavelet transform and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed. A super-resolution fusion algorithm is proposed to fuse two-dimensional magnetic signals to achieve image enhancement. Finally, the convolutional neural network is used to extract the features of the two types of images, and then the features are fused, and the support vector machine (SVM) is used to classify. Under the condition of zero broken wire error, compared with the subjectively extracted color features and texture features of the two types of images as the SVM input, this algorithm’s recognition rate is increased by 37.26%.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry and Materials Science</subject><subject>Complex Methods of Control</subject><subject>Image enhancement</subject><subject>Magnetic signals</subject><subject>Materials Science</subject><subject>Noise reduction</subject><subject>Nondestructive testing</subject><subject>Signal detection</subject><subject>Steel wire</subject><subject>Structural Materials</subject><subject>Support vector machines</subject><subject>Wavelet transforms</subject><subject>Wire rope</subject><issn>1061-8309</issn><issn>1608-3385</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LwzAYxoMoOKcfwFvAc_VNsibtUYfOwVBwAw8eSpq-KR1bMpNW8NubOcGDeHofeP7w8iPkksE1Y2Jys2QgWSGg5EICiLI8IiMmociEKPLjpJOd7f1TchbjGgC4EnxE3p68azD2YTB994F0lXTnWuotXfaIG_raBaQvfof0TkdsqHd0pluHfWfosmud3lDtGjp3NuiA38KHre47787JidWbiBc_d0xWD_er6WO2eJ7Np7eLzHBZ9JnkeW2UVAYmwqC1qhZMMJmjUMkXFqTUhuWGlVLlqGtVNFprMcmBNUrWYkyuDrO74N-H9H-19kNIj8WKF4kHqMQgpdghZYKPMaCtdqHb6vBZMaj2CKs_CFOHHzoxZV2L4Xf5_9IXFCJx5Q</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Zhang, Juwei</creator><creator>Chen, Quankun</creator><creator>Ye, Qiang</creator><general>Pleiades Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20230901</creationdate><title>Nondestructive Testing of Steel Wire Rope Based on Gagnetic Signal and Infrared Information</title><author>Zhang, Juwei ; Chen, Quankun ; Ye, Qiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c268t-625bc767c043ceff7b313165e372683f066ac15c19675eab78daaa34501d76b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Characterization and Evaluation of Materials</topic><topic>Chemistry and Materials Science</topic><topic>Complex Methods of Control</topic><topic>Image enhancement</topic><topic>Magnetic signals</topic><topic>Materials Science</topic><topic>Noise reduction</topic><topic>Nondestructive testing</topic><topic>Signal detection</topic><topic>Steel wire</topic><topic>Structural Materials</topic><topic>Support vector machines</topic><topic>Wavelet transforms</topic><topic>Wire rope</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Juwei</creatorcontrib><creatorcontrib>Chen, Quankun</creatorcontrib><creatorcontrib>Ye, Qiang</creatorcontrib><collection>CrossRef</collection><jtitle>Russian journal of nondestructive testing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Juwei</au><au>Chen, Quankun</au><au>Ye, Qiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nondestructive Testing of Steel Wire Rope Based on Gagnetic Signal and Infrared Information</atitle><jtitle>Russian journal of nondestructive testing</jtitle><stitle>Russ J Nondestruct Test</stitle><date>2023-09-01</date><risdate>2023</risdate><volume>59</volume><issue>9</issue><spage>991</spage><epage>1004</epage><pages>991-1004</pages><issn>1061-8309</issn><eissn>1608-3385</eissn><abstract>This paper designs a two-dimensional magnetic signal detection device under weak magnetic excitation, which solves the problem of large volume and single signal acquisition of traditional one-dimensional detection devices. To reduce the original noise, a noise reduction algorithm combining wavelet transform and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed. A super-resolution fusion algorithm is proposed to fuse two-dimensional magnetic signals to achieve image enhancement. Finally, the convolutional neural network is used to extract the features of the two types of images, and then the features are fused, and the support vector machine (SVM) is used to classify. Under the condition of zero broken wire error, compared with the subjectively extracted color features and texture features of the two types of images as the SVM input, this algorithm’s recognition rate is increased by 37.26%.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.1134/S1061830923600399</doi><tpages>14</tpages></addata></record> |
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| subjects | Algorithms Artificial neural networks Characterization and Evaluation of Materials Chemistry and Materials Science Complex Methods of Control Image enhancement Magnetic signals Materials Science Noise reduction Nondestructive testing Signal detection Steel wire Structural Materials Support vector machines Wavelet transforms Wire rope |
| title | Nondestructive Testing of Steel Wire Rope Based on Gagnetic Signal and Infrared Information |
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