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Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

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Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

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dc.contributor.author Antonino-Daviu, J. es_ES
dc.contributor.author Riera-Guasp, Martín es_ES
dc.contributor.author Pineda-Sanchez, Manuel es_ES
dc.contributor.author Pons Llinares, Joan es_ES
dc.contributor.author Puche-Panadero, Rubén es_ES
dc.contributor.author Pérez-Cruz, Juan es_ES
dc.date.accessioned 2018-03-13T05:06:57Z
dc.date.available 2018-03-13T05:06:57Z
dc.date.issued 2009 es_ES
dc.identifier.issn 1875-6883 es_ES
dc.identifier.uri http://hdl.handle.net/10251/99224
dc.description.abstract [EN] Recognition of characteristic patterns is proposed in this paper in order to diagnose the presence of electromechanical faults in induction electrical machines. Two common faults are considered; broken rotor bars and mixed eccentricities. The presence of these faults leads to the appearance of frequency components following a very characteristic evolution during the startup transient. The identification and extraction of these characteristic patterns through the Discrete Wavelet Transform (DWT) have been proven to be a reliable methodology for diagnosing the presence of these faults, showing certain advantages in comparison with the classical FFT analysis of the steady-state current. In the paper, a compilation of healthy and faulty cases are presented; they confirm the validity of the approach for the correct diagnosis of a wide range of electromechanical faults. es_ES
dc.description.sponsorship The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 under Grant Agreement n° 224233 (Research Project PRODI “Power plant Robustification based on fault Detection and Isolation algorithms”). The authors also thank ‘Vicerrectorado de Investigación, Desarrollo e Innovación of Universidad Politécnica de Valencia’ for financing a part of this research through the program ‘Programa de Apoyo a la Investigación y Desarrollo (PAID-06-07).
dc.language Inglés es_ES
dc.publisher Atlantis Press es_ES
dc.relation.ispartof International Journal of Computational Intelligence Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Electric machines es_ES
dc.subject Fault diagnosis es_ES
dc.subject Wavelet transform es_ES
dc.subject Broken bars es_ES
dc.subject Eccentricities es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.2991/ijcis.2009.2.2.7 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-06-07-3180/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/224233/EU/Power plants Robustification based On fault Detection and Isolation algorithms/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica es_ES
dc.description.bibliographicCitation Antonino-Daviu, J.; Riera-Guasp, M.; Pineda-Sanchez, M.; Pons Llinares, J.; Puche-Panadero, R.; Pérez-Cruz, J. (2009). Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT. International Journal of Computational Intelligence Systems. 2(2):158-167. https://doi.org/10.2991/ijcis.2009.2.2.7 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.2991/ijcis.2009.2.2.7 es_ES
dc.description.upvformatpinicio 158 es_ES
dc.description.upvformatpfin 167 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 2 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela S\36408 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


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