DETERMINING BEARING DEFECTS WITH DIGITAL IMAGE PROCESSING

Abstract

A major cause of poor quality and embarrassing situations for manufacturers are bearing flaws. The majority of the inspection procedures used in these businesses are laborious and manual. More thorough and accurate inspection procedures are needed to improve bearing defect identification accuracy. In order to detect potential defects, this research constructs a Bearing Defect Recognizer that combines local threshold holding with computer vision technology. The recognizer produces a less error-prone inspection method in real time and efficiently finds bearing problems. Primarily, the recognizer uses an image acquisition device to acquire digital bearing images and then transforms the RGB images into binary images using local threshold approaches and restoration processes. The area of the defective section and a computation of the potential defective and non-defective bearing are later outputs of the processed image. The results of the experiments demonstrate the proposed approach's sensitivity and dependability in identifying missing bearing balls as well as faults on the inner and outer races of bearings. The current system has a 94% accuracy rate.