## 1 Introduction  
Rolling bearing is a key part of rotary machinery. Previous studies had shown that rolling bearings were the cause of most of the faults of rotary equipment [1], and, therefore, it is of great significance to study the damage mechanism of rolling bearings. Skidding is a typical fault of high-speed light load rolling bearings [2], whose failure will induce the failure of the support shafting. Skidding is the first step to damage and will aggravate the friction between the roller and the raceway, increase the friction heat inside the bearing, decrease the viscosity of the lubricating oil, soften the material, and worsen the lubrication condition of the bearing and may eventually lead to skidding damage in bearings [3]. [PERSON] et al. [4] developed a high-speed rolling bearing skid damage test rig for the skidding problem of rolling bearings and carried out experimental research to study the influence of different factors on bearing temperature and skidding damage. Skidding damage of rolling bearings is an important source of excitation, inducing bearing vibration, which affects the vibration characteristics of rolling bearings [5; 6]. Nowadays, rolling bearing fault diagnosis technologies generally include diagnostic technologies based on acoustic signals, temperature signals, oil film signals, vibration signals, among others [7]. Of these, diagnosis technology based on vibration signal analysis is the most common in engineering applications, because the detection of vibration signal is relatively simple and intuitive, and hence the test and diagnosis technology based on vibration signal is most widely used in fault diagnosis [8]. At present, achievements have been made in the research of rolling bearing vibration characteristics. [PERSON] et al. [1; 9] compared various fault signal analysis methods, such as FFT, short-time fast Fouriertransform, envelope analysis, and empirical mode decomposition analysis to summarize the advantages and disadvantages of each diagnosis method in the study of the bearing fault analysis and identification. [PERSON] et al. [10] proposed a new rolling bearing fault diagnosis method based on Adaptive Minimum Entropy Deconvolution (AMED) and Time-Delayed Feedback Monostable Stochastic Resonance (TFMSR) in the study of rolling bearing faults and verified the possibility of this method through experiments. [PERSON] et al. [11] summarized and compared the application of fast Fourier transform and enhanced fast Fourier transform in the vibration signal analysis. The results showed that the improved fast Fourier transform has a better solution than the traditional fast Fourier transform. [PERSON] et al. [12] proposed the use of an accelerometer and a load cell to fuse two sensors for bearing fault diagnosis, effectively enhancing bearing fault detection and diagnosis ability. [PERSON] et al. [13] used wavelet packet decomposition, Fourier transform, and artificial neural network for machine fault diagnosis and prediction and showed that this method is more efficient and effective than the traditional method. [PERSON] [14] used multi-sensor information fusion technology for fault diagnosis, and its advantages and disadvantages and development trend were considered. It provided necessary basic knowledge for the follow-up study of rolling bearing fault diagnosis. [PERSON] [8] proposed a new rolling bearing fault diagnosis method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Multi-Scale Permutation Entropy (MPE) and Support Vector Machine (SVM), and developed a rolling bearing fault diagnosis system. [PERSON] [15] studied the bearing fault diagnosis method based on vibration signals and proposed a fault feature extraction and fault pattern recognition method for the rolling bearing fault. [PERSON] [16] developed fault classification methods based on signal processing and machine learning to solve the problem of feature extraction and fault classification of high-dimensional sample data of complex industrial systems.