You are in the accessibility menu

Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/129869
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCosta, Cesar da-
dc.contributor.authorKashiwagi, Masamori-
dc.contributor.authorMathias, Mauro Hugo-
dc.contributor.authorDestech Publicat Inc-
dc.date.accessioned2015-10-22T07:24:39Z-
dc.date.accessioned2016-10-25T21:16:40Z-
dc.date.available2015-10-22T07:24:39Z-
dc.date.available2016-10-25T21:16:40Z-
dc.date.issued2015-01-01-
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S2351988615000044-
dc.identifier.citationInternational Conference On Computer Science And Artificial Intelligence (iccsai 2014), Inc, p. 109-113, 2015.-
dc.identifier.urihttp://hdl.handle.net/11449/129869-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/129869-
dc.description.abstractThis paper presents two diagnostic methods for the online detection of broken bars in induction motors with squirrel-cage type rotors. The wavelet representation of a function is a new technique. Wavelet transform of a function is the improved version of Fourier transform. Fourier transform is a powerful tool for analyzing the components of a stationary signal. But it is failed for analyzing the non-stationary signal whereas wavelet transform allows the components of a non-stationary signal to be analyzed. In this paper, our main goal is to find out the advantages of wavelet transform compared to Fourier transform in rotor failure diagnosis of induction motors.en
dc.format.extent109-113-
dc.language.isoeng-
dc.publisherDestech Publications, Inc-
dc.sourceWeb of Science-
dc.subjectRotating electrical machineen
dc.subjectDiagnosticen
dc.subjectDigital signal processingen
dc.titleRotor Failure Diagnosis of Induction Motors by Wavelet Transform and Fourier Transform in Function of the Loaden
dc.typeoutro-
dc.contributor.institutionIFSP Fed Inst Educ-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationIFSP Fed Inst Educ, Electrotech Engn, BR-01109010 Sao Paulo, SP, Brazil-
dc.description.affiliationUniv Estadual Paulista, UNESP, Mech Engn, Sao Paulo, SP, Brazil-
dc.description.affiliationUnespUniv Estadual Paulista, UNESP, Mech Engn, Sao Paulo, SP, Brazil-
dc.identifier.wosWOS:000351732900024-
dc.rights.accessRightsAcesso aberto-
dc.identifier.fileWOS000351732900024.pdf-
dc.relation.ispartofInternational Conference On Computer Science And Artificial Intelligence (iccsai 2014)-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

There are no files associated with this item.
Show simple item record Show full item record   View Statistics
 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.