Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection
Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tao, Jin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
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| Anmerkung: |
© MAIK “Nauka/Interperiodica” 2005 |
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| Übergeordnetes Werk: |
Enthalten in: The Russian journal of nondestructive testing - Nauka/Interperiodica, 1992, 41(2005), 4 vom: Apr., Seite 231-238 |
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| Übergeordnetes Werk: |
volume:41 ; year:2005 ; number:4 ; month:04 ; pages:231-238 |
| Links: |
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| DOI / URN: |
10.1007/s11181-005-0155-0 |
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| Katalog-ID: |
OLC2067588338 |
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| 520 | |a Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. | ||
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10.1007/s11181-005-0155-0 doi (DE-627)OLC2067588338 (DE-He213)s11181-005-0155-0-p DE-627 ger DE-627 rakwb eng 670 VZ Tao, Jin verfasserin aut Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2005 Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. Neural Network Structural Material Dynamic Network Recognition Algorithm Orthogonal Wavelet Peiwen, Que aut Liang, Chen aut Liang, Li aut Enthalten in The Russian journal of nondestructive testing Nauka/Interperiodica, 1992 41(2005), 4 vom: Apr., Seite 231-238 (DE-627)131135708 (DE-600)1124967-5 (DE-576)032746954 1061-8309 nnns volume:41 year:2005 number:4 month:04 pages:231-238 https://doi.org/10.1007/s11181-005-0155-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_4116 AR 41 2005 4 04 231-238 |
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10.1007/s11181-005-0155-0 doi (DE-627)OLC2067588338 (DE-He213)s11181-005-0155-0-p DE-627 ger DE-627 rakwb eng 670 VZ Tao, Jin verfasserin aut Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2005 Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. Neural Network Structural Material Dynamic Network Recognition Algorithm Orthogonal Wavelet Peiwen, Que aut Liang, Chen aut Liang, Li aut Enthalten in The Russian journal of nondestructive testing Nauka/Interperiodica, 1992 41(2005), 4 vom: Apr., Seite 231-238 (DE-627)131135708 (DE-600)1124967-5 (DE-576)032746954 1061-8309 nnns volume:41 year:2005 number:4 month:04 pages:231-238 https://doi.org/10.1007/s11181-005-0155-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_4116 AR 41 2005 4 04 231-238 |
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10.1007/s11181-005-0155-0 doi (DE-627)OLC2067588338 (DE-He213)s11181-005-0155-0-p DE-627 ger DE-627 rakwb eng 670 VZ Tao, Jin verfasserin aut Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2005 Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. Neural Network Structural Material Dynamic Network Recognition Algorithm Orthogonal Wavelet Peiwen, Que aut Liang, Chen aut Liang, Li aut Enthalten in The Russian journal of nondestructive testing Nauka/Interperiodica, 1992 41(2005), 4 vom: Apr., Seite 231-238 (DE-627)131135708 (DE-600)1124967-5 (DE-576)032746954 1061-8309 nnns volume:41 year:2005 number:4 month:04 pages:231-238 https://doi.org/10.1007/s11181-005-0155-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_4116 AR 41 2005 4 04 231-238 |
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10.1007/s11181-005-0155-0 doi (DE-627)OLC2067588338 (DE-He213)s11181-005-0155-0-p DE-627 ger DE-627 rakwb eng 670 VZ Tao, Jin verfasserin aut Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2005 Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. Neural Network Structural Material Dynamic Network Recognition Algorithm Orthogonal Wavelet Peiwen, Que aut Liang, Chen aut Liang, Li aut Enthalten in The Russian journal of nondestructive testing Nauka/Interperiodica, 1992 41(2005), 4 vom: Apr., Seite 231-238 (DE-627)131135708 (DE-600)1124967-5 (DE-576)032746954 1061-8309 nnns volume:41 year:2005 number:4 month:04 pages:231-238 https://doi.org/10.1007/s11181-005-0155-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_4116 AR 41 2005 4 04 231-238 |
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10.1007/s11181-005-0155-0 doi (DE-627)OLC2067588338 (DE-He213)s11181-005-0155-0-p DE-627 ger DE-627 rakwb eng 670 VZ Tao, Jin verfasserin aut Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2005 Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. Neural Network Structural Material Dynamic Network Recognition Algorithm Orthogonal Wavelet Peiwen, Que aut Liang, Chen aut Liang, Li aut Enthalten in The Russian journal of nondestructive testing Nauka/Interperiodica, 1992 41(2005), 4 vom: Apr., Seite 231-238 (DE-627)131135708 (DE-600)1124967-5 (DE-576)032746954 1061-8309 nnns volume:41 year:2005 number:4 month:04 pages:231-238 https://doi.org/10.1007/s11181-005-0155-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_4116 AR 41 2005 4 04 231-238 |
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Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. © MAIK “Nauka/Interperiodica” 2005 |
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Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. © MAIK “Nauka/Interperiodica” 2005 |
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Abstract Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented. © MAIK “Nauka/Interperiodica” 2005 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2067588338</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504035315.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2005 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11181-005-0155-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2067588338</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11181-005-0155-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Tao, Jin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Research on a Recognition Algorithm for Offshore-Pipeline Defects during Magnetic-Flux Inspection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2005</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© MAIK “Nauka/Interperiodica” 2005</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Pipeline-safety evaluation is an important problem for industry. 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