## 2 Bearing Failure Modes
### Vision Detection  
Visual inspection, as a highly automated detection method, utilizes machine vision technology to detect the appearance and surface defects of bearings [66]. The principle of visual inspection is shown in Figure 9.  
Image acquisition: When the bearing is in operation or stopped, the system uses high-resolution cameras to capture key parts of the bearing, such as the inner and outer rings and rolling element surfaces. When shooting, parameters such as light source and camera angle can be adjusted to ensure clear image quality, especially highlighting areas where defects may occur for subsequent processing.  
Image preprocessing: The preprocessing steps include denoising, contrast enhancement, binarization, etc., with the aim of highlighting defect features and eliminating interference caused by the environment or lighting. For example, binary processing can distinguish defective areas from normal areas, improving the accuracy of subsequent detection.  
Image feature extraction: Edge detection, contour extraction, texture analysis, and other techniques are used to identify the shape, size, and location of surface defects on bearings [67].  
Image pattern matching: Matching techniques include template matching and deep learning matching. Template matching is the process of comparing the detected image with a standard template to identify differences that are suitable for detecting specific defect patterns. For example, cracks may appear as elongated edge features, while worn areas typically exhibit irregular texture features. The machine vision system will compare these features with standard samples or preset parameters and analyze them through algorithms to determine if there are any abnormalities. [PERSON] et al. proposed a method for detecting and classifying surface defects of bearings using machine vision technology [68]. To detect  
Figure 9: Principle of vision detection: Uses high-definition cameras or industrial cameras to capture images of bearing surfaces, and then identify cracks, wear, scratches, contaminants, or other surface defects through image processing and algorithm analysis.  
surface defects in subway vehicle rolling bearings, on-site images of black magnetic particle testing defects in subway vehicle rolling bearings were collected. [PERSON] and others utilized image processing technology to extract all binary images of surface defects and established a dataset that includes both the original images of surface defects and the binary images. It was pointed out that there are significant difficulties and limitations in using traditional machine vision technology to identify defects in subway bearings. Therefore, convolutional neural networks were chosen to identify bearing defects in order to achieve deep feature fusion. Through comparisons with several models, it was concluded that there is a certain improvement in the effectiveness and accuracy of surface defect recognition [69].  
The advantage of vision inspection is that it can accurately identify small surface defects, and the detection process is non-contact, suitable for detection under complex working conditions such as high temperature and high speed. Its high degree of automation is suitable for real-time quality monitoring of production lines, which can improve production efficiency and reduce manual errors. However, visual inspection often relies on complex hardware and algorithm support and has low sensitivity to deep-seated internal defects or small cracks that are not directly exposed. It usually requires the combination of other detection methods, such as ultrasonic or eddy current testing, to achieve comprehensive diagnosis.