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An unsupervised clustering methodology by means of an improved dbscan algorithm for operational conditions classification in a structure

dc.contributor.advisorSierra Pérez, Julián
dc.contributor.authorPerafan López, Juan Carlos
dc.coverage.spatialSeccional Medellín. Escuela de Ingenierías. Maestría en Ingenieríases_CO
dc.date.accessioned2018-09-20T16:07:18Z
dc.date.available2018-09-20T16:07:18Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/20.500.11912/4017
dc.description117 Páginases_CO
dc.description.abstractStructural Health Monitoring (SHM) is highly relevant nowadays, not only for aerospace maintenance, but for a large number of newly engineering applications. Pattern recognition has become an important part of SHM for signal processing and anomalies or damage detection, assuring structural integrity. New methods are created day by day and more researchers and engineers feel the interest to generate techniques which can make SHM become a more compacted, sophisticated and automatized system, eliminating human factors and intrinsic errors. This work evaluates the computational complexity and accuracy of a novel methodology of unsupervised clustering called FA+GA-DBSCAN which employs a combination of machine learning techniques including factor analysis for dimensionality reduction and a density clustering algorithm called DBSCAN enhanced with a genetic algorithm. In order to automatically detect a variety of structural behaviors using the novel methodology an experiment with a beam in cantilever under dynamic loads was taken in consideration.es_CO
dc.formatapplication/pdfes_CO
dc.language.isospaes_CO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceinstname:Universidad Pontificia Bolivarianaes_CO
dc.sourcereponame:Repositorio Institucional de la Universidad Pontificia Bolivarianaes_CO
dc.subjectMonitoreo de salud estructurales_CO
dc.subjectReconocimiento de modeloses_CO
dc.subjectAprendizaje automático (Inteligencia artificial)es_CO
dc.subjectLocalización de fallas (Ingeniería)es_CO
dc.subjectAlgoritmos genéticoses_CO
dc.subjectAnálisis de datoses_CO
dc.titleAn unsupervised clustering methodology by means of an improved dbscan algorithm for operational conditions classification in a structurees_CO
dc.typemasterThesises_CO
dc.creator.degreeMagister en Ingenieríases_CO
dc.publisher.departmentEscuela de Ingenieríases_CO
dc.publisher.programMaestría en Ingenieríaes_CO
dc.rights.accessRightsopenAccesses_CO
dc.type.hasVersiondraftes_CO
dc.description.sectionalMedellínes_CO


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International