An experimental study, about detection of bearing defects in inverter fed small induction motors by Concordia transform |
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Authors: | ?zzet Yilmaz Önel Engin Ayçiçek ?brahim ?enol |
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Affiliation: | (1) Electrical-Electronics Faculty, Electrical Engineering Department, Yildiz Technical University, 34349 Besiktas-Istanbul, Turkey |
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Abstract: | This paper describes an application about detection of bearing defects in inverter fed induction motors, using Concordia transform
approach based algorithm. After introduction, brief information is given about bearing structure and type of bearing failures.
Next section, Concordia transform theory is mentioned then, RBF neural network structure is summarized. After that, test system
information is specified. This paper indicates that Concordia transform approach is a reliable tool to detect bearing faults
in inverter fed small induction motors. The generality of the proposed methodology has been experimentally tested on a 1 HP
squirrel-cage induction motor. At the end of the paper, an ANN algorithm is proposed that could detect the bearing faults
automatically. The obtained results have 93.75% accuracy. This study suggests that proposed Concordia transform based fault
detection algorithm could be integrated in an induction motor driver so, bearing condition of the induction motor could be
observed while motor is working and bearing faults could be detect before they become serious. |
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Keywords: | Induction motor Bearing faults Concordia transform RBF neural network |
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