Performance Prediction of Induction Motor Due to Rotor Slot Shape Change Using Convolution Neural Network

Abstract

We propose a method to predict performance variables according to the rotor slot shape of a three-phase squirrel cage induction motor using a convolution neural network (CNN) algorithm suitable for utilizing image data. The set of performance variables was labeled according to the images of each training dataset, and this set was generated from the efficiency, power factor, starting torque, and average torque. To verify the accuracy of the trained deep learning model, the analysis and prediction results of the CNN model were compared and verified with nine untrained double cage slot shapes and shapes optimized based on the root mean square error (RMSE). Although a large number of training data are required for high accuracy in the existing image processing deep learning model, the proposed deep learning method can predict the performance variables for various shapes with the same level of accuracy as the finite element analysis results using a small number of training data. Therefore, it is expected to be applied in various engineering fields.