Bearing Surface Defect Detection System Based On LabVIEW

Bearings are an important key component in industrial production.Major bearing production and assembly companies attach great importance to the qualification rate of bearings.At present,manual inspection methods are mainly used for the detection of assembly bearing defects.However,the manual inspection method is not only inefficient,but also the experience of the inspectors will have a greater impact on the inspection results,making the accuracy of defect detection lower.(1)To address the problems of noise and low image contrast caused by the complex environment of bearing assembly.Use image graying for processing.Then use grayscale image enhancement,image smoothing and other methods to process.Binary processing is performed on the processed grayscale image after performing circle detection.(2)After analyzing the characteristics of the assembled bearing image,an improved RANSAC-based circle detection algorithm is used to perform circle detection on the image to find the center of the assembled bearing.During the assembly bearing defect detection stage,six types of assembly bearing defects were analyzed based on binary images,and the obtained pixel line maps were used to extract and classify defect features.Finally,the image information perturbation was designed to reorganize,remove interference and fill gaps Way to deal with.(3)To meet the needs of on-site inspection,set up the image acquisition environment,apply the detection method designed in this paper to the software planning of the assembly bearing defect detection system.In this paper,five different types of assembled bearings with the same structure are selected as the test objects,and the detection method used in this article is used for testing.The test accuracy rate reached 99.9%,which met the on-site testing requirements.