Size Detection Method of Embedded Ball Based on Aviation Bearing Image

Abstract

In the aerospace industry, bearing is widely used in various rotating machinery. The performance of bearing affects the operation of the whole machinery and even aviation equipment. The wrongly assembled ball due to size is an important reason for unqualified bearing. To solve this problem, an accurate ball detection method based on the bearing image is proposed. Firstly, according to the imaging characteristics of bearing and light propagation characteristics, an image collection system based on the coaxial light source is designed. Then, aiming at the problem that the embedded ball is occluded by the bearing ring and the cage, only partial ball in the narrow gap can be used to predict the full ball and the high-precision requirement of ball detection, a ball segmentation model based on DeepLab v3+ network is used to segment the local ball, and CBAM is added in the Xception network of the original network. According to the characteristics of the segmentation result, a circle detection algorithm based on circle fitting evaluation designed for incomplete short arc is proposed. Finally, according to the detection results, judge whether the bearing is qualified or not and evaluate the feasibility of this method. Experimental results show that the ball detection accuracy is about 27 microns, and the wrongly assembled ball with a size difference of only 198 microns can be distinguished. The false detection rate of unqualified bearing is 1%. As the last line of defense of bearing quality inspection, this method can achieve zero false detection rate of unqualified bearing in the industry.