Bearing defect inspection based on machine vision

Abstract

Appearance defects inspection plays a vital role in bearing quality control. Human inspection is a traditional way to remove defective bearings, which is instable and time consuming. In this paper, we develop a machine vision system for bearing defect inspection, which can inspect various types of defects on bearing covers, such as deformations, rusts, scratches and so on. The proposed system designs a novel image acquisition system to enhance the defects appearances and get controlled image acquisition environment. A series of image processing methods are proposed or utilized to inspect the defects. Especially, for the deformation defects on seal, we find a common rule on the distribution of projection, and design a simple but effective inspection algorithm based on the rule. The proposed system is evaluated and compared with skilled human by the recall, precision and F-measure. Experimental results show that the proposed vision system has high accuracy and efficiency.