## 1 Introduction
Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators. [PERSON] et al. [32] proposed a novel bearing fault diagnosis method based on deep CNN and random forest ensemble learning. [PERSON] et al. [33] proposed a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout regularization. Based on the previously proposed problem of dataset selection, this paper conducts a health state classification study based on the XJTU-SY bearing dataset as the data basis for bearing fault diagnosis. For the health state classification method of bearings, domestic and foreign scholars have also conducted relevant research. The most commonly used method to classify the health state is the observation method, and [PERSON] [34] classified the bearing operating health state at different stages by observing the vibration signal. Although this is the simplest and most intuitive method, the error of the division is large. [PERSON] [35] used HHT to build the power spectral density of the bearing signal, used quartic polynomial fitting, and derived the fitting curve. The two inflection points of the curve are taken as the multi-stage dividing points of bearing health. [PERSON] et al. [36; 37] constructed different types of health factors for deep-step feature extraction of vibration signals, and used the 3 principle to classify the bearing health state by observing the sudden change points in the smooth phase and the smooth phase signals within the threshold value. Some scholars also used the HMM model to divide the bearing health status into two stages and multi-stage processes [38; 39; 40]. The feature processing capability, adaptability, computational efficiency, and interpretability of existing deep learning methods in bearing fault diagnosis still need to be improved, and the corresponding research still needs to be perfected. Based on the problems summarized above and the current status of the research, the research idea of this paper is proposed. To better expand the dataset, this paper performs health state segmentation on the XJTU-SY bearing dataset as the database for bearing fault diagnosis. In this paper, the one-dimensional signal characteristics of the data are retained in data preprocessing, and two network models, CNN and RNN, are used as the core, and Dropout, BN, and other techniques are introduced to jointly solve the bearing fault diagnosis problem. Under the condition of ensuring the diagnostic accuracy of the model, the noise resistance performance of the model is studied by adding noises with different SNRs to simulate different noise environments of actual industrial scenes. By comparing with other algorithmic models, it is verified that the method proposed in this paper has strong fault diagnosis and anti-noise capability. The rest of this paper is organized as follows: Section 2 introduces the proposed bearing health status division method. Section 3 introduces the proposed deep learning network model. Section 4 is the experimental verification part of the Sections 2 and 3. Finally, conclusions are given in Section 5.