Data mining approach for supply unbalance detection in induction motor

This paper describes an approach for detection of the supply unbalance condition in induction motors by using data mining process. Simulation results have shown that a good indicator of the fault is the amplitude of the second harmonic of the supply frequency component (2f) in the signal obtained by the differences in supply current zero crossing instants. In the study, linear regression (LR), pace regression (PR), sequential minimal optimization (SMO), M5 model tree, M5’Rules, KStar, additive regression and back propagation neural network (BPNN) models are applied within the data mining process for determining the condition of the motor supply voltage. All data mining algorithms were applied using WEKA software. The best result for the determination of the fault related dominant parameter was obtained by using the M5P algorithm model.