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Análisis e interpretación de señales de fuga de campo magnético en tuberías de transporte de hidrocarburos para la detección de pérdidas significativas de material

dc.contributor.advisorGonzález Acevedo, Hernando
dc.contributor.authorQuintero Plata, Javier Mario
dc.coverage.spatialColombiaspa
dc.date.accessioned2022-07-19T17:00:50Z
dc.date.available2022-07-19T17:00:50Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/20.500.12749/17019
dc.description.abstractDiseñar clasifcadores basados en redes neuronales y Análisis Discriminante Lineal (por sus síglas en ingles LDA) para identificar pérdidas de material en tuberías de transporte de hidrocarburos, por medio de un análisis espectral de las señales de MFL.spa
dc.description.tableofcontentsObjetivos Introducción Procesamiento de señlaes MFL Señales MFL Base de datos Diseño de clasificadores Conclusiones Bibliografía Anexosspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleAnálisis e interpretación de señales de fuga de campo magnético en tuberías de transporte de hidrocarburos para la detección de pérdidas significativas de materialspa
dc.title.translatedAnalysis and interpretation of magnetic field leakage signals in hydrocarbon transport pipelines for detection of significant material lossesspa
dc.degree.nameIngeniero Mecatrónicospa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.programPregrado Ingeniería Mecatrónicaspa
dc.description.degreelevelPregradospa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsMechatronicspa
dc.subject.keywordsMFL signalsspa
dc.subject.keywordsNeural networksspa
dc.subject.keywordsHydrocarbon transportationspa
dc.subject.keywordsAlgorithmsspa
dc.subject.keywordsMachine theoryspa
dc.subject.keywordsArtificial intelligencespa
dc.subject.keywordsMathematical modelsspa
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.relation.references[1] Arizmendi P., Carlos Julio. SIGNAL PROCESSING TECHNIQUES FOR BRAIN TUMOUR DIAGNOSIS FROM MAGNETIC RESONANCE SPECTROSCOPY DATA. 2011. Ph. D. Tesís.spa
dc.relation.references[2] Feng, QAingshan. Sutherland, Jeff. Gu, Bill. Wei, Yao. Tao, Cui. EVOLUTION OF TRIAX MAGNETIC FLUX LEAKAGE INSPECTION FOR MITIGATION OF SPIRAL WELD ANOMALIES. En: Pipeline Technology Conference. 2011.spa
dc.relation.references[3] YOU, Zhongqing. VENCZEL, John. BODEN, Dudley. Advanced NDE forPipes and Tubes as per API Specifications. En: Proceedings of the National Seminar & Exhibition on Non-Destructive Evaluation. Diciembre, 2009.spa
dc.relation.references[4] Arizmendi P., Carlos Julio. Transformada Wavelet. Facultad de Ingenierías Fisicomecánicas. UNAB. 2013.spa
dc.relation.references[5] Donoho, David L. Different Perspectives on Wavelets. En: American Mathematical Society, Estados Unidos. 1993.spa
dc.relation.references[6] MUKHOPADHYAY, S. SRIVASTAVA, 6G.P. Characterisation of metal loss defects from magnetic flux leakage signals with discrete wavelet transform. En: NDT&E. No 33 (2002); p. 57-65.spa
dc.relation.references[7] L. S. Correa, B. F. Giraldo, E. Laciar, A. Torres. Multi-parameter Analysis of ECG and Respiratory Flow Signals to Identify Success of Patients on Weaning Trials. En: Annual International Conference of the IEEE EMBS. No. 32 (2010); p. 6070-6073.spa
dc.relation.references[8] W. Couch, Leon. Digital & Analog Communication Systems. 8 ed. Prentice Hall, 2012.spa
dc.relation.references[9] Métodos de análisis de datos en ecología. Departamento de ecología. Universidad de Alcalá. 2005spa
dc.relation.references[10] Vidal, Tomás. Introducción a las Redes Neuronales. Departamento Electrónica. UTFSM. 2012.spa
dc.relation.references[11] 8. Balakrishnama, A. Ganapathiraju. LINEAR DISCRIMINANT ANALYSIS - A BRIEF TUTORIAL. Institute for Signal and Information Processing. Department of Electrical and Computer Engineering. Mississippi State University.spa
dc.relation.references[12] Tibshirani, Hastie. Cross-validation and bootstrap. SLDM 111. Febrero, 2009.spa
dc.relation.references[13] Kay H. Brodersen, Cheng Soon Ong, Klaas E. Stephan, Joachim M. Buhmann. The balanced accuracy and its posterior distribution. En: International Conference on Pattern Recognition. 2010.spa
dc.relation.references[14] Neural Network Toolbox. Function Aproximation and Nonlinear Regression. Choose a Multilayer Neural Network Training Function. En: Documentation - Matlab® 2013a [en línea]. <htip://Amww.mathworks.com/help/nnet/ug/choose-a- multilayer-neural-network-training-function.html> [citado el 2 de enero de 2015].spa
dc.contributor.cvlacGonzález Acevedo, Hernando [0000544655]spa
dc.contributor.googlescholarGonzález Acevedo, Hernando [V8tga0cAAAAJ&hl=es&oi=ao]spa
dc.contributor.orcidGonzález Acevedo, Hernando [0000-0001-6242-3939]spa
dc.contributor.scopusGonzález Acevedo, Hernando [55821231500]spa
dc.contributor.researchgateGonzález Acevedo, Hernando [Hernando-Gonzalez-Acevedo-2199006362]spa
dc.subject.lembMecatrónicaspa
dc.subject.lembAlgoritmosspa
dc.subject.lembTeoría de las máquinasspa
dc.subject.lembInteligencia artificialspa
dc.subject.lembModelos matemáticosspa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.description.abstractenglishDesign classifcators based on neural networks and Linear Discriminant Analysis (LDA) to identify material losses in hydrocarbon transport pipelines, through a spectral analysis of MFL signals.spa
dc.subject.proposalSeñales MFLspa
dc.subject.proposalRedes neuronalesspa
dc.subject.proposalTransporte de hidrocarburosspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TP
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.description.learningmodalityModalidad Presencialspa


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