## 1 Introduction  
Rolling bearings play an important role in the operation of large-scale equipment. As the most common component of rotating machinery and equipment, rolling bearings play the role of transmission and support, and their working conditions have a significant impact on the operating conditions of the entire equipment. Rolling bearings are widely used in mechanical production and other fields. There is a lag in the maintenance of bearings after their failure. Once the production is shut down, it can cause a lot of time and cost waste to the enterprise. It is necessary to detect failings with bearings in advance and warn relevant personnel in time for factories and enterprises. However, rolling bearings are also one of the most easily damaged parts. The accidents caused by them are difficult to recover from. Not only does production need to stop for maintenance and to replace parts, but also the maintenance costs are relatively high. According to statistics, approximately 30% of vibration faults in rotating machinery are caused by rolling bearing faults [1]. It can be seen that if the fault of the rolling bearing can be found and eliminated in time, the fault rate of the equipment will be greatly reduced. Therefore, it is of realistic research significance to research the fault diagnosis of rolling bearings. The fault diagnosis technology of rolling bearings has been researched and developed for a long time, and there are three development stages in summary. The first stage of development is mainly manual fault detection and analysis of equipment. In the early stages of industrial development, there were no sophisticated fault diagnosis instruments and fault detection could only be performed by experienced professionals. Due to the interference of human factors, this method has a certain degree of subjectivity, and there are often situations such as judgment errors or ignoring certain faults [2]. As a result, the fault diagnosis rate and adaptability are relatively low, causing abnormal heating, stalling, and abnormal vibration of engineering equipment, resulting in huge economic and personnel losses. The second stage of development, first of all, is the time-domain analysis method. Commonly used time-domain characteristic index types include mean value, probability distribution, impulse index, density function, root mean square frequency, peak value, and kurtosis. Many scholars have conducted a lot of research on extracting time-domain features. [PERSON] et al. [3] proposed a new method for predicting the fault state of rolling bearings by monitoring the vibration frequency and amplitude of the bearing seat. The method takes the kurtosis of the calculated vibration signal as the fault feature, and the kurtosis of the undamaged rolling bearing remains unchanged. The degree of damage to the bearing can be evaluated by the distribution of the kurtosis parameter in the selected frequency range. [PERSON] et al. [4] proposed that the periodic vibration peak generated by the rolling bearing at a constant speed is used as the fault characteristic signal, and the detection of the health status of the rolling bearing by the signal peak can improve the accuracy of diagnosis. [PERSON] et al. [5] took kurtosis and mean square root as feature vectors at the same time, which can improve the fault recognition rate of rolling bearings. The fault diagnosis method based on time-domain features has the advantages of a simple calculation and fast diagnosis. However, it is difficult to extract effective fault information when extracting nonlinear and non-stationary vibration signal features. The next step is a frequency domain analysis. Many scholars have found many effective methods in the in-depth research and discussion of the frequency domain analysis method, such as amplitude-frequency spectrum, full pedigree, empirical mode decomposition, variational mode decomposition, envelope analysis, wavelet transform, etc. [6]. Empirical mode decomposition (EMD) has a good processing effect on time-domain and frequency domain data.