An experimental study, about detection of bearing defects in inverter fed small induction motors by Concordia transform

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.