An improved gated convolutional neural network for rolling bearing fault diagnosis with imbalanced data

Abstract

To improve the ability of the deep learning model to handle imbalanced data, a fault diagnosis method based on improved gated convolutional neural network (IGCNN) is proposed. Firstly, an improved gated convolution layer is proposed for feature extraction, with the batch normalisation (BN) layer applied to adjust the data distribution and enhance the generalisation performance of the model. Then, the feature learned by multiple gated convolution layers and pooling layers is fed to the fully connected layer for fault type identification. Finally, the label-distribution-aware margin (LDAM) loss function is employed to adjust the model being more sensitive to the minority class and mitigate the influence of imbalanced data on the model. Experimental validation is conducted using two bearing datasets. Results show that the proposed method is more robust than other fault diagnosis methods, with higher recognition accuracy in severely imbalanced dataset.