Intelligent fault diagnosis of rolling mills based on dual attention- guided deep learning method under imbalanced data conditions

As an important link in the steel production chain, the health of the rolling mill directly affects the steel production. Therefore, the study of rolling mill fault diagnosis methods is of great significance to improve the continuity, reliability and safety of production. However, in the case of uneven data distribution, in order to improve the recognition performance, the traditional fault diagnosis method has developed the deep network architecture of convolutional neural network, which is not easy to obtain accurate fault characteristics and it is difficult to achieve better recognition accuracy. Aiming at these problems, we propose a rolling mill fault diagnosis method based on time–frequency image and Dual Attention-guided Feature Enhancement Network (DAFEN). First of all, the original one-dimensional vibration signal is converted into two-dimensional time–frequency images and used as the input of the network, and then the DAFAE is designed to analyze and integrate all convolutional features to complete the fault identification, in order to verify the superiority of the proposed method, we verified based on balanced datasets and imbalanced datasets, and our model was at least 0.71% and 1.43% higher than the highest accuracy fault classification results of other advanced CNN models.