## 1 Introduction
In recent years, inspired by the design idea of AlexNet, many researchers have added activation functions, Dropout [21], Batch Normalization (BN), and other techniques based on the CNN to enhance the strong nonlinear feature extraction ability of the neural network, and many representative convolutional neural networks such as VggNet and GoogleNet have appeared [22]. In 2016, [PERSON] et al. [23] proposed the residual convolutional neural network (ResNet). This network won first place in several tracks in ILSVRC and COCO 2015 competitions: ImageNet detection, ImageNet localization, COCO detection, and COCOsegmentation. This network can add jump connections to deeper network layers at any network layer, which avoids the loss of data feature information and overfitting phenomenon. This greatly increases the depth of the network model, which leads to more superior network models. A recurrent neural network (RNN) is also one of the representative algorithms of deep learning. The network structure is connected in a ring with a node orientation, and the internal state can display dynamic timing behavior. It is often applied to the processing of timing information such as audio and text. However, since RNN encounters the problem of gradient disappearance when processing long time series information, the network has only short-term memory. The Long Short-Term Memory (LSTM) network improved by [PERSON] [24] effectively solves the problem of short memory, but the LSTM network is more complicated. [PERSON] et al. [25] proposed the Gate Recurrent Unit (GRU) network structure. The network simplifies the LSTM network structure and has long-term memory. Currently, in the field of rolling bearing fault diagnosis research, deep learning is widely used for research related to mining time series of vibration signal data. [PERSON] et al. [26] proposed a hybrid model based on CNN and individual classifiers to diagnose bearing faults. Experiments have verified that random forest (RF) and support vector machine (SVM) can make full use of the feature extraction ability of CNN. The average diagnosis accuracy of the CNN-RF model and CNN-SVM model on the large-scale dataset is 98.9% and 99%, respectively. [PERSON] et al. [27] proposed a new multi-source TL model, which uses feature learners to generate features of each source domain and target domain data, so that the joint weight classifier can predict target tags. A distance metric based on moment matching is also introduced to reduce the distance between all source domains and target domains. Experimental results, such as high diagnostic accuracies of 99.96%, support the reliability and universality of the proposed model. [PERSON] et al. [28] proposed a depth feature reinforcement learning method for rolling bearing fault diagnosis. Using the Elu activation function and attention mechanism model, they established a depth Q network to accurately diagnose the fault mode. The test accuracy of the proposed method was the highest, and the average test accuracy reached 98.71%, which shows that this method is superior to other intelligent diagnosis methods. [PERSON] et al. [29] proposed a rolling bearing fault diagnosis model that combines a recursive neural network based on two-stage attention and a convolutional block attention module. In the experimental test of CWRU, results indicate that the accuracy of the proposed fault diagnosis method DARN-CBAM-CNN for rolling bearings is 97.69% and the proposed fault diagnosis method has broad application prospects under the condition of unbalanced data. [PERSON] et al. [30] proposed an intelligent diagnosis algorithm based on CNN, which can automatically accomplish the process of the feature extraction and fault diagnosis. [PERSON] et al. [31] proposed a fault diagnosis method based on the fusion of deep learning with a knowledge graph.