MFL signals and artificial neural networks applied to detection and classification of pipe weld defects
- Author
- CARVALHO, A. A1 ; REBELLO, J. M. A1 ; SAGRILO, L. V. S2 ; CAMERINI, C. S3 ; MIRANDA, I. V. J4
[1] Department of Metallurgical and Materials Engineering, COPPE/ UFRJ-Federal University of Rio de Janeiro, P.O. Box 68505, CEP 21941-972, Rio de Janeiro RJ, Brazil
[2] Department of Civil Engineering, COPPE/UFRJ-Federal University of Rio de Janeiro, P.O. Box 68505, CEP 21941-972, Rio de Janeiro RJ, Brazil
[3] PETROBRAS Research and Development Center (CENPES), Cidade Universitdria, Q. 7, Ilha do Fundão, CEP 21949-900, Rio de Janeiro RJ, Brazil
[4] Pipe Way Engineering, Praça Mario Nazaré, 40 São Cristovão, CEP 20940-080, Rio de Janeiro RJ, Brazil - Source
NDT & E international. 2006, Vol 39, Num 8, pp 661-667, 7 p ; ref : 22 ref
- ISSN
- 0963-8695
- Scientific domain
- Mechanical engineering
- Publisher
- Elsevier Science, Oxford
- Publication country
- United Kingdom
- Document type
- Article
- Language
- English
- Author keyword
- MFL Neural network Non-destructive inspection Pattern recognition Pig
- Keyword (fr)
- Acier Analyse Fourier Analyse signal Assemblage soudé Canalisation Classification Corrosion Défaut soudure Essai non destructif Essai électromagnétique Filtre Flux dispersion magnétique Métrologie Pipeline Reconnaissance forme Réseau neuronal Traitement image Traitement signal Transformation ondelette Tuyauterie
- Keyword (en)
- Steel Fourier analysis Signal analysis Welded joint Pipe Classification Corrosion Weld defect Non destructive test Electromagnetic testing Filter Magnetic leakage flux Metrology Pipeline Pattern recognition Neural network Image processing Signal processing Wavelet transformation Piping
- Keyword (es)
- Acero Análisis Fourier Análisis de señal Ensamblaje soldado Canalización Clasificación Corrosión Defecto soldadura Ensayo no destructivo Filtro Flujo dispersión magnética Metrología Oleoducto Reconocimiento patrón Red neuronal Procesamiento imagen Procesamiento señal Transformación ondita Cañería
- Classification
- Pascal
- 001 Exact sciences and technology / 001B Physics / 001B40 Fundamental areas of phenomenology (including applications) / 001B40C Acoustics / 001B40C60 Acoustic signal processing
- Pascal
- 001 Exact sciences and technology / 001B Physics / 001B40 Fundamental areas of phenomenology (including applications) / 001B40F Solid mechanics / 001B40F30 Structural and continuum mechanics / 001B40F30R Measurement and testing methods
- Pascal
- 001 Exact sciences and technology / 001B Physics / 001B80 Cross-disciplinary physics: materials science; rheology / 001B80A Materials science / 001B80A70 Materials testing
- Discipline
- Physics : acoustics Physics : solid mechanics Physics and materials science
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 18165836
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Access to the document
Install on your browser
DOI
10.1016/j.ndteint.2006.04.003 
https://dx.doi.org/10.1016/j.ndteint.2006.04.003
"O:13:\"PanistOpenUrl\":36:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000idc\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:29:\"10.1016\/j.ndteint.2006.04.003\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:9:\"\u0000*\u0000jtitle\";s:21:\"NDT & E international\";s:9:\"\u0000*\u0000stitle\";s:9:\"NDT E int\";s:7:\"\u0000*\u0000date\";s:4:\"2006\";s:9:\"\u0000*\u0000volume\";s:2:\"39\";s:8:\"\u0000*\u0000issue\";s:1:\"8\";s:8:\"\u0000*\u0000spage\";s:3:\"661\";s:8:\"\u0000*\u0000epage\";s:3:\"667\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0963-8695\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:8:\"CARVALHO\";s:10:\"\u0000*\u0000aufirst\";s:4:\"A. A\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:4:{i:0;s:16:\"REBELLO, J. M. A\";i:1;s:16:\"SAGRILO, L. V. S\";i:2;s:14:\"CAMERINI, C. S\";i:3;s:16:\"MIRANDA, I. V. J\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:0:\"\";s:8:\"\u0000*\u0000title\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:8:\"\u0000*\u0000place\";s:6:\"Oxford\";s:6:\"\u0000*\u0000pub\";s:16:\"Elsevier Science\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"7\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Panist access
"O:12:\"IstexOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:29:\"10.1016\/j.ndteint.2006.04.003\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:9:\"\u0000*\u0000jtitle\";s:21:\"NDT & E international\";s:9:\"\u0000*\u0000stitle\";s:9:\"NDT E int\";s:7:\"\u0000*\u0000date\";s:4:\"2006\";s:9:\"\u0000*\u0000volume\";s:2:\"39\";s:8:\"\u0000*\u0000issue\";s:1:\"8\";s:8:\"\u0000*\u0000spage\";s:3:\"661\";s:8:\"\u0000*\u0000epage\";s:3:\"667\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0963-8695\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:8:\"CARVALHO\";s:10:\"\u0000*\u0000aufirst\";s:4:\"A. A\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:4:{i:0;s:16:\"REBELLO, J. M. A\";i:1;s:16:\"SAGRILO, L. V. S\";i:2;s:14:\"CAMERINI, C. S\";i:3;s:16:\"MIRANDA, I. V. J\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:0:\"\";s:8:\"\u0000*\u0000title\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:8:\"\u0000*\u0000place\";s:6:\"Oxford\";s:6:\"\u0000*\u0000pub\";s:16:\"Elsevier Science\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"7\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Istex access
"O:16:\"UnpaywallOpenUrl\":35:{s:8:\"\u0000*\u0000email\";s:27:\"assistance-portail@inist.fr\";s:6:\"\u0000*\u0000fmt\";N;s:6:\"\u0000*\u0000doi\";s:29:\"10.1016\/j.ndteint.2006.04.003\";s:6:\"\u0000*\u0000pii\";N;s:7:\"\u0000*\u0000pmid\";N;s:9:\"\u0000*\u0000atitle\";N;s:9:\"\u0000*\u0000jtitle\";N;s:9:\"\u0000*\u0000stitle\";N;s:7:\"\u0000*\u0000date\";N;s:9:\"\u0000*\u0000volume\";N;s:8:\"\u0000*\u0000issue\";N;s:8:\"\u0000*\u0000spage\";N;s:8:\"\u0000*\u0000epage\";N;s:8:\"\u0000*\u0000pages\";N;s:7:\"\u0000*\u0000issn\";N;s:8:\"\u0000*\u0000eissn\";N;s:9:\"\u0000*\u0000aulast\";N;s:10:\"\u0000*\u0000aufirst\";N;s:9:\"\u0000*\u0000auinit\";N;s:10:\"\u0000*\u0000auinitm\";N;s:5:\"\u0000*\u0000au\";N;s:9:\"\u0000*\u0000aucorp\";N;s:7:\"\u0000*\u0000isbn\";N;s:8:\"\u0000*\u0000coden\";N;s:8:\"\u0000*\u0000genre\";N;s:7:\"\u0000*\u0000part\";N;s:9:\"\u0000*\u0000btitle\";N;s:8:\"\u0000*\u0000title\";N;s:8:\"\u0000*\u0000place\";N;s:6:\"\u0000*\u0000pub\";N;s:10:\"\u0000*\u0000edition\";N;s:9:\"\u0000*\u0000tpages\";N;s:9:\"\u0000*\u0000series\";N;s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";N;}"
Unpaywall access
"O:16:\"EuropePMCOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:29:\"10.1016\/j.ndteint.2006.04.003\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:9:\"\u0000*\u0000jtitle\";s:21:\"NDT & E international\";s:9:\"\u0000*\u0000stitle\";s:9:\"NDT E int\";s:7:\"\u0000*\u0000date\";s:4:\"2006\";s:9:\"\u0000*\u0000volume\";s:2:\"39\";s:8:\"\u0000*\u0000issue\";s:1:\"8\";s:8:\"\u0000*\u0000spage\";s:3:\"661\";s:8:\"\u0000*\u0000epage\";s:3:\"667\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0963-8695\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:8:\"CARVALHO\";s:10:\"\u0000*\u0000aufirst\";s:4:\"A. A\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:4:{i:0;s:16:\"REBELLO, J. M. A\";i:1;s:16:\"SAGRILO, L. V. S\";i:2;s:14:\"CAMERINI, C. S\";i:3;s:16:\"MIRANDA, I. V. J\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:0:\"\";s:8:\"\u0000*\u0000title\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:8:\"\u0000*\u0000place\";s:6:\"Oxford\";s:6:\"\u0000*\u0000pub\";s:16:\"Elsevier Science\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"7\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Europe PubMed Central access
"O:12:\"ArXivOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:29:\"10.1016\/j.ndteint.2006.04.003\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:9:\"\u0000*\u0000jtitle\";s:21:\"NDT & E international\";s:9:\"\u0000*\u0000stitle\";s:9:\"NDT E int\";s:7:\"\u0000*\u0000date\";s:4:\"2006\";s:9:\"\u0000*\u0000volume\";s:2:\"39\";s:8:\"\u0000*\u0000issue\";s:1:\"8\";s:8:\"\u0000*\u0000spage\";s:3:\"661\";s:8:\"\u0000*\u0000epage\";s:3:\"667\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0963-8695\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:8:\"CARVALHO\";s:10:\"\u0000*\u0000aufirst\";s:4:\"A. A\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:4:{i:0;s:16:\"REBELLO, J. M. A\";i:1;s:16:\"SAGRILO, L. V. S\";i:2;s:14:\"CAMERINI, C. S\";i:3;s:16:\"MIRANDA, I. V. J\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:0:\"\";s:8:\"\u0000*\u0000title\";s:103:\"MFL signals and artificial neural networks applied to detection and classification of pipe weld defects\";s:8:\"\u0000*\u0000place\";s:6:\"Oxford\";s:6:\"\u0000*\u0000pub\";s:16:\"Elsevier Science\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"7\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
arXiv access
