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Bearing Defect Inspection Seminar Report
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.
Bearing defect inspection based on Machine vision
In the industry of machinery, bearings are important components that connect different machine parts to reduce frictions. They have been widely used in air conditioners, cars, and many other rotating machines. The quality of bearings can directly influence the performance of many machines, and may even cause serious disasters. Bearings are usually mass-produced with high demand of precision, and a lot of inspection measures have been adopted in the production process to ensure the quality of bearings. The inspection measures can be classified into three steps: material inspection, assembling inspection and final goods inspection. The material inspection is mainly focused on the dimension and surface inspection of the receiving materials, such as inner rings, outer rings and balls. The assembling inspection is used to inspect the defects that are caused by assembling process, including surface inspection and vibration test. The final goods inspection is mainly focused on the surface defects, giving a full inspection before packing, so the result of final goods inspection can directly influence the product quality. Currently, the inspection of bearings in manufacture mainly depends on skilled human inspectors with the help of bright lights. The manual activity of inspection is subjective and highly dependent on the experience of human inspectors, which cannot provide a guarantee of quality. In addition, working under bright lights for a long time is harmful to human’s health. Therefore, automatically inspecting the defects of bearings becomes an important issue, and the computer vision can play a crucial role in it. Machine vision has made great progresses in the past few decades. Much work has been done in this field and some inspection techniques for uniform surface have already been used in industries, for example, the inspection of textiles, and steel detection.
However, for the products with low contrast property or complex shape, there is still much work left to be done. In this paper, we propose a machine vision system for the inspection of bearing cover defects. A novel lighting and image acquisition system is designed to enhance the defects appearance and get controlled environments. Then, a series of image processing methods are proposed or utilized to inspect the defects on bearing. Especially, for the deformation defects on seal, we find a common rule on the distribution of projection, and propose a simple but effective inspection algorithm based on the rule. The proposed system is capable of inspecting various types of appearance defects on bearing covers, including deformations, scratches, cracks and rusts.
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