A Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolution
Magnetic flux leakage (MFL) detection of rail surface defects is an important research field for railway traffic safety. Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detecti...
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| Veröffentlicht in: | International journal of swarm intelligence research 2024-01, Vol.15 (1), p.1-11 |
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| creator | Liu, Jing Su, Shoubao Guo, Haifeng Lu, Yuhua Chen, Yuexia |
| description | Magnetic flux leakage (MFL) detection of rail surface defects is an important research field for railway traffic safety. Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved. |
| doi_str_mv | 10.4018/IJSIR.332791 |
| format | Article |
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Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved.</description><identifier>ISSN: 1947-9263</identifier><identifier>EISSN: 1947-9271</identifier><identifier>DOI: 10.4018/IJSIR.332791</identifier><language>eng</language><publisher>Hershey: IGI Global</publisher><subject>Accuracy ; Algorithms ; Background noise ; Deconvolution ; Defects ; Entropy ; Fault diagnosis ; Feature selection ; Filtration ; Leak detection ; Magnetic fields ; Magnetic flux ; Magnetization ; Mathematical analysis ; Methods ; Noise ; Noise control ; Noise generation ; Nondestructive testing ; Railroads ; Signal processing ; Signal strength ; Surface defects ; Wavelet transforms</subject><ispartof>International journal of swarm intelligence research, 2024-01, Vol.15 (1), p.1-11</ispartof><rights>COPYRIGHT 2024 IGI Global</rights><rights>This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (htpp://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.</rights><rights>2024. 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Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Background noise</subject><subject>Deconvolution</subject><subject>Defects</subject><subject>Entropy</subject><subject>Fault diagnosis</subject><subject>Feature selection</subject><subject>Filtration</subject><subject>Leak detection</subject><subject>Magnetic fields</subject><subject>Magnetic flux</subject><subject>Magnetization</subject><subject>Mathematical analysis</subject><subject>Methods</subject><subject>Noise</subject><subject>Noise control</subject><subject>Noise generation</subject><subject>Nondestructive testing</subject><subject>Railroads</subject><subject>Signal processing</subject><subject>Signal strength</subject><subject>Surface defects</subject><subject>Wavelet transforms</subject><issn>1947-9263</issn><issn>1947-9271</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU1rGzEQQJfSQkOSW3-AoNc6lTT62qPjxo2DTSFuz0KWtRul65UjaUsD-fGRu8G5hM5BI5inETOvqj4RfMEwUV8XN-vF7QUAlTV5V52QmslJTSV5f7wL-Fidp3SPS3AmJYeT6mmK1r7tTYfmvssu-r5FK5fvwhY1IaKVaXuXvUXzbviLls78Nq1D31x2NvvQo9CgW-M7tB5iY-yh0pRKQpcmuS0qwMr3fjfs0FWfY9g_FsCG_k_ohsPzs-pDY7rkzl_yafVrfvVzdj1Z_vi-mE2XEwsK8qSRwA2jHGMJEjZbaWqxwURSYQUFQ7mwgPlWGSKww0RYBwSMFE5ZDnRD4bT6PPbdx_AwuJT1fRhiGTppWmPGFEjyf0opqgQjtSrUl5FqTef0Zki-d6kcybd3ObVmSElPpWSMAC8bP-I2hpSia_Q--p2Jj5pgffCm_3nTo7eCz0bct_71-4MfffSjRz-6-HmrB-HwDNDdn5A</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Liu, Jing</creator><creator>Su, Shoubao</creator><creator>Guo, Haifeng</creator><creator>Lu, Yuhua</creator><creator>Chen, Yuexia</creator><general>IGI Global</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-2036-7646</orcidid><orcidid>https://orcid.org/0000-0001-6359-9442</orcidid></search><sort><creationdate>20240101</creationdate><title>A Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolution</title><author>Liu, Jing ; 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Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. 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| subjects | Accuracy Algorithms Background noise Deconvolution Defects Entropy Fault diagnosis Feature selection Filtration Leak detection Magnetic fields Magnetic flux Magnetization Mathematical analysis Methods Noise Noise control Noise generation Nondestructive testing Railroads Signal processing Signal strength Surface defects Wavelet transforms |
| title | A Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolution |
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