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Título
Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling
Año del Documento
2017-05
Documento Fuente
IEEE Transactions on Industry Applications, May/Jun 2017, 53, 3, 3066-3075,
Abstract
Abstract—Intelligent fault detection in induction motors (IMs) is a widely studied research topic.
Various artificial-intelligence- based approaches have been proposed to deal with a large amount of
data obtained from destructive laboratory testing. However, in real applications, such volume of
data is not always available due to the effort required in obtaining the predictors for classifying
the faults. Therefore, in realistic scenarios, it is necessary to cope with the small-data problem,
as it is known in the literature. Fault- related instances along with healthy state observations
obtained from the IM compose datasets that are usually imbalanced, where the number of instances
classified as the faulty class (minority) is much lower than those classified under the healthy
class (ma- jority). This paper presents a novel supervised classification ap- proach for IM faults
based on the adaptive boosting algorithm with an optimized sampling technique that deals with the
imbal- anced experimental dataset. The stator current signal is used to compose a dataset with
features both from the time domain and from the frequency domain. The experimental results
demonstrate that the proposed approach achieves higher performance metrics than others classifiers
used in this field for the incipient detection and classification of faults in IM.
Index Terms—Classification algorithms, condition monitoring, data analysis, fault diagnosis,
induction motors (IMs), rotors, sam-
ISSN
0093-9994
Revisión por pares
SI
Version del Editor
Idioma
spa
Tipo de versión
info:eu-repo/semantics/acceptedVersion
Derechos
openAccess
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