## 2 Bearing Failure Modes  
As early as the 1970s, [PERSON] and [PERSON] began to focus on the failure modes and diagnostic techniques of bearings [3]. In the first two decades of the 21 st century, Gegner studied typical failure modes such as wear and contact fatigue of bearings under extreme working conditions [4]. [PERSON] et al. provided important data on the impact of lubrication state changes on bearing life and performance [5]. [PERSON] studied the performance and failure of bearings from the perspective of material microstructure [6]. In recent years, [PERSON] et al. have further comprehensively summarized the failure modes, mechanisms, and causes of bearing faults [7]. Summarizing common failure modes and analyzing the core causes of these failures is an important step in bearing fault detection. The common failure modes of rolling bearings, as also presented in Figure 2, include the following:  
* Fatigue Spalling  
The microcracks on the rolling elements and raceway surfaces, caused by repeated loads during the operation process, eventually expand into spalling. Fatigue spalling can significantly increase vibration and noise, leading to unstable equipment operation and potentially damaging adjacent components, accelerating the overall failure of the equipment. * Thermal Damage  
It is caused by excessively high temperatures or temperature fluctuations during operation. This can lead to a significant increase in the friction coefficient, and the resulting surface deformation and abnormal gaps can exacerbate bearing vibrations and noise, potentially triggering other failure modes. For instance, high temperatures may result in lubrication failure, accelerate the reaction of corrosive media, or promote the propagation of fatigue cracks. * Plastic Deformation  
When the load exceeds the yield strength of the material, permanent deformation occurs. Plastic deformation can compromise the original precision of the bearing, leading  
Figure 2: Bearing failure modes. signal through time domain techniques, frequency domain techniques, time-frequency domain techniques, and techniques combined with intelligent methods [13]. The detection principle of vibration analysis is shown in Figure 3.  
Time domain analysis is simple and intuitive, focusing on the characteristics of signal changes over time. It can reflect the characteristics of vibration signal changes over a period of time by statistical parameters such as peak value, peak factor, skewness, root mean square, and time domain energy [14]. By observing the statistical parameter values, diagnosis can be achieved. Frequency domain analysis can clearly identify fault characteristic frequencies. Bearings produce different characteristic frequencies due to different fault locations such as inner and outer rings, rolling elements, and retainers, as well as different failure modes such as wear and cracks. These frequencies are also related to the geometric parameters, rotational speed, contact angle, etc., of the bearing, and can be calculated using existing formulas. When these characteristic frequencies appear in the vibration signal, it indicates that there is a fault in the corresponding part of the bearing [15]. For example, the power spectral density (PSD) function is a model commonly used to describe the spectral characteristics of vibration signals in frequency domain analysis. Fourier transform is a key tool for converting vibration signals from the time domain to the frequency domain. Combining the advantages of the above two techniques, time-frequency domain analysis not only contains time-varying information, but also characteristic frequencies, so it can handle non-stationary signals [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Common methods include short-time Fourier transform and wavelet transform. Traditional vibration analysis methods have been combined with intelligent methods such as machine learning, deep learning, and data-driven approaches [29; 30].