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Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

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Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

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dc.contributor.author Georgoulas, G. es_ES
dc.contributor.author Mustafa, M.O. es_ES
dc.contributor.author Tsoumas, I.P. es_ES
dc.contributor.author Antonino Daviu, José Alfonso es_ES
dc.contributor.author Climente Alarcón, Vicente es_ES
dc.contributor.author Stylios, C.D. es_ES
dc.contributor.author Nikolakopoulos, G. es_ES
dc.date.accessioned 2015-07-01T09:24:52Z
dc.date.available 2015-07-01T09:24:52Z
dc.date.issued 2013-11-01
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10251/52546
dc.description.abstract This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator s three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator s current independently of the motor s load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Expert Systems with Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Broken rotor bar fault diagnosis es_ES
dc.subject Principal Component Analysis es_ES
dc.subject Hidden Markov Modeling es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.eswa.2013.06.006
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Ingeniería Energética - Institut d'Enginyeria Energètica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica es_ES
dc.description.bibliographicCitation Georgoulas, G.; Mustafa, M.; Tsoumas, I.; Antonino Daviu, JA.; Climente Alarcón, V.; Stylios, C.; Nikolakopoulos, G. (2013). Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines. Expert Systems with Applications. 40(17):7024-7033. doi:10.1016/j.eswa.2013.06.006 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.eswa.2013.06.006 es_ES
dc.description.upvformatpinicio 7024 es_ES
dc.description.upvformatpfin 7033 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 40 es_ES
dc.description.issue 17 es_ES
dc.relation.senia 254166


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