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Por favor, use este identificador para citar o enlazar este ítem: http://uvadoc.uva.es/handle/10324/25941
Título: Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts
Autor: García Escudero, Luis Ángel
Duque Pérez, Oscar
Fernández Temprano, Miguel
Moríñigo Sotelo, Daniel
Año del Documento: 2017
Descripción: Producción Científica
Documento Fuente: IEEE Transactions on Industry Applications, Vol 53(3), 3076-3085.
Resumen: A considerable amount of papers has been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper, a new method is proposed, which is based on robust statistical techniques applied in quality control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques, which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked: a broken bar, a faulty bearing, and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults.
Revisión por Pares: SI
DOI: 10.1109/TIA.2016.2617300
Patrocinador: MINECO and FEDER program grants DPI2014-52842-P, MTM2015-71217-R, MTM2014-56235-C2-1-P
Consejería de Educación de la Junta de Castilla y León under Grant VA212U13
Version del Editor: http://ieeexplore.ieee.org/document/7590081/
Propietario de los Derechos: IEEE
Idioma: eng
URI: http://uvadoc.uva.es/handle/10324/25941
Derechos: info:eu-repo/semantics/restrictedAccess
Aparece en las colecciones:DEP24 - Artículos de revista

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