Diagnosis of Induction Motor Faults Based On Current and Vibration Signals Using Support Vector Machine Model

Abstract

Early fault diagnosis of the induction motor can prevent sudden failure of the motor, which implies loss of production and sometimes brings safety problems. The purpose of this paper is to explore a method for induction motor fault diagnosis using a support vector machine model. The raw current and vibration signals are pre-processed using variational mode decomposition to eliminate the noise. Eight features in the time domain are extracted from the signals. These features are then evaluated using principal component analysis to reduce their dimension. Two principal components that cover 95% of the variance of the data are used as predictors in the SVM model. SVM models with different types of kernels are evaluated for their performance. The results show that the current signals give better accuracy in diagnosing induction motor faults than the vibration signals. The current signals perform very well at all speeds and in all types of kernels. Their accuracy is 100% for training and testing data. Meanwhile, the accuracy of the vibration signal in diagnosing the motor faults is good at speeds of 749 rpm, and the diagnosis accuracy decreases at speeds of 1499 rpm.