RESEARCH ON BEARING FAULT DIAGNOSIS UNDER UNBALANCED DATA SET BASED ON IWAE (MT)

Abstract

Aiming at the low accuracy with unbalanced data sets in existing bearing fault diagnosis methods, we proposed a bearing fault diagnosis method based on importance weighted auto-encoder（IWAE） in unbalanced data sets. It was trained by minority samples, and the generated samples were added into original data sets to obtain balanced data sets. Then, deep learning method was used as diagnose network, and the balanced data sets were fed into it as input, so as to adaptively learn fault characteristics and realize fault classification. A large number of qualitative experiments showed that when the imbalance rate was 1∶7, the method could correctly classify the balanced samples, and the accuracy rate was 98.90%. Based on various imbalance ratios, the proposed method had better convergence and generalization than other existing models.

Keywords

• Unbalanced data set;Importance weighted autoencoders;One-dimensional multi-scale convolutional neural network;Bearing fault diagnosis