Karvelis, P.; Georgoulas, G.; Tsoumas, TP.; Antonino Daviu, JA.; Climente Alarcón, V.; Stylios, CD. (2015). A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors. IEEE Transactions on Industrial Informatics. 11(5):1028-1037. https://doi.org/10.1109/TII.2015.2463680
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/71905
Title:
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A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors
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Author:
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Karvelis, Petros
Georgoulas, George
Tsoumas, Toannis P.
Antonino Daviu, José Alfonso
Climente Alarcón, Vicente
Stylios, Chrysostomos D.
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UPV Unit:
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Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica
Universitat Politècnica de València. Instituto de Ingeniería Energética - Institut d'Enginyeria Energètica
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Issued date:
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Abstract:
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One of the most common deficiencies of currently
existing induction motor fault diagnosis techniques is their lack
of automatization. Many of them rely on the qualitative interpretation
of the results, a fact that ...[+]
One of the most common deficiencies of currently
existing induction motor fault diagnosis techniques is their lack
of automatization. Many of them rely on the qualitative interpretation
of the results, a fact that requires significant user expertise,
and that makes their implementation in portable condition monitoring
devices difficult. In this paper, we present an automated
method for the detection of the number of broken bars of an induction
motor. The method is based on the transient analysis of the
start-up current using wavelet approximation signal that isolates
a characteristic component that emerges once a rotor bar is broken.
After the isolation of this component, a number of stages are
applied that transform the continuous-valued signal into a discrete
one. Subsequently, an intelligent icon-like approach is applied
for condensing the relative information into a representation that
can be easily manipulated by a nearest neighbor classifier. The
approach is tested using simulation as well as experimental data,
achieving high-classification accuracy.
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Subjects:
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Discrete wavelet transform
,
Intelligent icons
,
Piecewise aggregate approximation (PAA)
,
Rotor faults
,
Symbolic
representation
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Copyrigths:
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Cerrado |
Source:
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IEEE Transactions on Industrial Informatics. (issn:
1551-3203
) (eissn:
1941-0050
)
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DOI:
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10.1109/TII.2015.2463680
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Publisher:
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Institute of Electrical and Electronics Engineers (IEEE)
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Publisher version:
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http://dx.doi.org/10.1109/TII.2015.2463680
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Project ID:
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info:eu-repo/grantAgreement/MINECO//DPI2014-52842-P/ES/COMBINACION DE TECNICAS NO INVASIVAS DE MONITORIZACION DEL ESTADO PARA EL DESARROLLO DE MOTORES ELECTRICOS INTELIGENTES/
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Description:
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"(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
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Thanks:
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This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER program in the framework of the Proyectos I+D del Subprograma de Generacion de Conocimiento, Programs Estatal de ...[+]
This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER program in the framework of the Proyectos I+D del Subprograma de Generacion de Conocimiento, Programs Estatal de Foment de la Investigacion Cientifica y Tecnica de Excelencia (ref: DPI2014-52842-P). Paper no. TII-14-0766.
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Type:
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Artículo
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