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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 |