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Please use this identifier to cite or link to this item: http://uvadoc.uva.es/handle/10324/25941
Title: Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts
Authors: García Escudero, Luis Ángel
Duque Pérez, Oscar
Fernández Temprano, Miguel
Moríñigo Sotelo, Daniel
Issue Date: 2017
Description: Producción Científica
Citation: IEEE Transactions on Industry Applications, Vol 53(3), 3076-3085.
Abstract: 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.
Peer Review: SI
DOI: 10.1109/TIA.2016.2617300
Sponsor: 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
Publisher Version: http://ieeexplore.ieee.org/document/7590081/
Rights Owner: IEEE
Language: eng
URI: http://uvadoc.uva.es/handle/10324/25941
Rights: info:eu-repo/semantics/restrictedAccess
Appears in Collections:DEP24 - Artículos de revista

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