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Support vector machine classifier for diagnosis in electrical machines: Application to broken bar

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Support vector machine classifier for diagnosis in electrical machines: Application to broken bar

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dc.contributor.author Matic, Dragan es_ES
dc.contributor.author Kulic, Filip es_ES
dc.contributor.author Pineda Sánchez, Manuel es_ES
dc.contributor.author Kamenko, Ilija es_ES
dc.date.accessioned 2016-01-11T11:57:05Z
dc.date.available 2016-01-11T11:57:05Z
dc.date.issued 2012-08
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10251/59639
dc.description.abstract [EN] This paper presents a support vector machine classifier for broken bar detection in electrical induction machine. It is a reliable online method, which has high robustness to load variations and changing operating conditions. The phase current is only physical value to be measured. The steady state current is analyzed for broken bar fault via motor current signature analysis technique based on Hilbert transform. A two dimensional feature space is proposed. The features are: magnitude and frequency of characteristic peak extracted from spectrum of Hilbert transform series of the phase current. For classification task support vector machine is used due to its good robustness and generalization performances. A comparative analysis of linear, Gaussian and quadratic kernel function versus error rate and number of support vectors is done. The proposed classifier successfully detects a broken bar in various operational situations. The proposed method is sufficiently accurate, fast, and robust to load changes, which makes it suitable for use in real-time online applications in industrial drives. es_ES
dc.description.sponsorship This paper is produced as a result of work on FP7 project for area of information and communication technologies named "PRODI - Power plants Robustification based on fault Detection and Isolation algorithms" contract number 224233 financed from European Comity, General Manager for Information community and Media. en_EN
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 bar es_ES
dc.subject Fault detection es_ES
dc.subject Support vector machines es_ES
dc.subject Induction motor es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Support vector machine classifier for diagnosis in electrical machines: Application to broken bar es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.eswa.2012.01.214
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/224233/EU/Power plants Robustification based On fault Detection and Isolation algorithms/ es_ES
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 Matic, D.; Kulic, F.; Pineda Sánchez, M.; Kamenko, I. (2012). Support vector machine classifier for diagnosis in electrical machines: Application to broken bar. Expert Systems with Applications. 39(10):8681-8689. https://doi.org/10.1016/j.eswa.2012.01.214 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.eswa.2012.01.214 es_ES
dc.description.upvformatpinicio 8681 es_ES
dc.description.upvformatpfin 8689 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 39 es_ES
dc.description.issue 10 es_ES
dc.relation.senia 239867
dc.contributor.funder European Commission


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