A gray texture image data-driven intelligent fault diagnosis method of induction motor rotor-bearing system under variable load conditions

Abstract

Induction motors often operate in variable conditions, and the data distribution of diverse conditions is different. To solve the issue of low accuracy of diagnosis methods caused by large differences in data distribution, an intelligent fault diagnosis method of induction motor rotor-bearing system is proposed. The method combines convolutional neural network with adaptive batch normalization, and the gray texture images transformed from vibration signals are used as the input samples. A total of thirteen conditions including constant and time-varying loads are designed to verify the performance of the method. The average accuracy of the model for thirteen conditions of test set reaches 98.7 %, and the average accuracy for eight unknown conditions of test set is 99.0 %. The proposed method can extract features that are not affected by the variable load conditions, facilitate the transfer from no-load to on-load and known to unknown conditions.