Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods

Abstract

In the present work, stator winding fault prediction is studied using a multiscale entropy (MSE) algorithm combined with a grey-based fuzzy algorithm. Experiments were performed with a normal motor and a motor with faulty stator winding. Real time, motor current and vibration signals were acquired at different operating speeds and were used for the diagnosis of faults. The obtained signals were denoised by wavelet transform. Grey relational analysis (GRA) coupled with fuzzy logic was used to model the stator winding fault and to predict the optimal setting for running the induction motor within its parameters range. Analysis of variance (ANOVA) was performed to determine the effect of each individual parameter on the response. The results indicate that the proposed novel approach is very effective in predicting the stator winding fault. Furthermore, the best running parameters for the induction motor are also reported.