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dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributor.authorCardona Morales, Oscar
dc.contributor.authorÁlvarez Marín, Diego Andrés
dc.contributor.authorCastellanos Domínguez, Germán
dc.date.accessioned2019-06-28T09:45:46Z
dc.date.available2019-06-28T09:45:46Z
dc.date.issued2013
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/40994
dc.description.abstractThe main goal of condition-based maintenance is to describe the machine state under current operating regimes, which can be non-stationary depending of load/speed changes. Besides, damaged machine data are not always available in real-world applications. This paper proposes a methodology of outlier detection in time-varying mechanical systems based on dynamic features and data description classifiers. Dynamic features set is formed by spectral sub-band centroids and linear frequency cepstral coefficients extracted from time-frequency representations. One-class classification is carried out to validate performance of the dynamic features as descriptors of machine behavior. The methodology is tested with a data set coming from a test-rig including different machine states with variable speed conditions. The proposed approach is validated on real recordings acquired from a ship driveline. The results outperform other time-frequency features in terms of classification performance. The methodology is robust to minimal changes in the machine state and/or time-varying operational conditions.
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia Sede Medellín
dc.relationhttp://revistas.unal.edu.co/index.php/dyna/article/view/30181
dc.relation.ispartofUniversidad Nacional de Colombia Revistas electrónicas UN Dyna
dc.relation.ispartofDyna
dc.relation.ispartofseriesDyna; Vol. 80, núm. 182 (2013); 173-181 DYNA; Vol. 80, núm. 182 (2013); 173-181 2346-2183 0012-7353
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleOutlier detection in rotating machinery under non-stationary operating conditions using dynamic features and one-class classifiers
dc.typeArtículo de revista
dc.type.driverinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.eprintshttp://bdigital.unal.edu.co/31091/
dc.relation.referencesCardona Morales, Oscar and Álvarez Marín, Diego Andrés and Castellanos Domínguez, Germán (2013) Outlier detection in rotating machinery under non-stationary operating conditions using dynamic features and one-class classifiers. Dyna; Vol. 80, núm. 182 (2013); 173-181 DYNA; Vol. 80, núm. 182 (2013); 173-181 2346-2183 0012-7353 .
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalDynamic features
dc.subject.proposalOne-class classification
dc.subject.proposalData description
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-NoComercial 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit