Research on partition method of bearing circumferential surface based on machine vision

Abstract

As a key component of industrial machinery, the surface defects of bearings will directly af-fect the safety of industrial production, so it is of great significance to carry out quality inspec-tion of bearings in actual industrial production. Because the defects in different positions on the circumferential surface have different damage degrees to the bearing, the defects in the core area of the circumferential surface are the most harmful. Therefore, based on machine vision inspection, which is the mainstream apparent quality inspection technology of industrial prod-ucts.       The essence of the bearing circumferential partitioning task is to accurately extract the up-per and lower edges of the bearing in the image. In order to solve the problems of uneven image illumination, oil stain and bump at the bearing edge in the bearing edge extraction task, this the-sis proposes a bearing demarcation line detection algorithm based on adaptive Butterworth homomorphic filtering and multi-scale Gaussian line fusion. Firstly, to address the problem of uneven bearing illumination, this thesis proposes an image adaptive threshold segmentation al-gorithm based on Butterworth homomorphic filtering. The Gaussian filter in the homomorphic filter is replaced by the Butterworth filter, which effectively improves uneven illumination. Then, in combination with the adaptive threshold segmentation algorithm, the appropriate parameters were automatically selected to preprocess the image and enhance the edge feature information. Subsequently, to eliminate the influence of defects, this thesis proposes a demarcation line coarse localisation algorithm based on a class-aware hashing algorithm. The algorithm auto-matically locates near the bearing demarcation line and selects the appropriate region of inter-est to minimise the false edge problem caused by defects. Finally, to address the issue of bearing demarcation line fracture, a multi-scale bearing edge detection algorithm based on Gaussian line is proposed. The algorithm accurately extracts the coordinates of the dividing line by fusing the convolution map of multi-scale Gaussian derivative images, and uses the least squares method for curve fitting. Through the above method, the accuracy of the final bearing partition reached 99.23%.