## 1 Introduction  
The bearing is one of the most critical parts of rotating machinery. If a fault occurs in use, it will seriously affect the normal operation of mechanical equipment, causing huge losses and disasters. Therefore, bearing fault diagnosis is an essential step for the normal operation of modern rotating machinery. Research on bearing diagnosis methods and fault mechanisms to ensure the normal operation of bearings has always been a key issue of concern for domestic and foreign experts. Nowadays, machinery system health monitoring has stepped into the era of big data, which is manifested in the use of sensors to obtain monitoring sample data, deep learning to accumulate training experience as the main technical means, and intelligent judgment of machinery health status as the ultimate goal, to ensure the reliability of equipment operation and promote efficient production. At present, bearing fault diagnosis technologies mainly include vibration analysis, acoustic analysis, oil sample analysis, temperature analysis, and voltage current detection [1]. The vibration analysis method is a widely used diagnosis method, in which the vibration signal can be measured online and combined with deep learning technology feature extraction to determine the early failure type of the bearing. However, due to the high cost of data acquisition, the need for storage and transmission technology to be developed, and other reasons, the typical vibration signal fault diagnosis dataset is extremely scarce,which seriously restricts the theoretical research and engineering application of mechanical equipment health management technology and fault diagnosis. In the past 20 years, the research on deep learning fault diagnosis of bearing vibration signals has mainly included the following three aspects:  
1. Selection of datasets  
The Western Reserve University dataset [2] is one of the most studied datasets for bearing fault diagnosis by many scholars. However, single dataset research also hinders the research of bearing fault diagnosis algorithms. More and more scholars are also working on newer fault diagnosis datasets. The Prognostics and Health Management (PHM) 2012 bearing full-cycle life dataset (FEMTO-ST) [3] is the most used dataset in full-cycle life prediction studies, but the disadvantages of this dataset are that the failure location is not given and the sampling duration is only 0.1 s. The frequency resolution is low and it is not possible to perform fault diagnosis classification studies. The University of Cincinnati bearing dataset [4], which contains the full cycle life and failure location of bearings, is generally used for bearing remaining life prediction studies. It is also used by more and more scholars in the field of fault classification. However, this dataset was only obtained under a single operating condition with constant values of both rotational speed and radial load, and the sample size is small. Moreover, many scholars are discouraged by the long time of disclosure and the large amount of data. Professor [PERSON]'s team in the School of Mechanical Engineering, at Xi'an Jiaotong University, publicly released the XJTU-SY dataset [5], which contains the full life-cycle vibration signals of 15 rolling bearings under three working conditions. The motor speed for condition 1 is 2100 r/min and the radial force is 12 KN; the speed for condition 2 is 2250 r/min and the radial force is 11 KN; the speed for condition 3 is 2400 r/min and the radial force is 10 KN. The dataset is clearly labeled with the failure location of each bearing, which provides data support for the research in the field of PHM and promotes the algorithm research in the field of bearing remaining useful life prediction [6; 7; 8; 9]. However, for the bearing failure locations given in this dataset, no scholars have conducted reasonable fault diagnosis classification studies.