Gyroscope Pivot Bearing Dimension and Surface Defect Detection

Sensors

Wenqian Ge¹  Huijie Zhao² 

[1] Precision Opto-Mechatronics Technology, Key-Laboratory of Education Ministry, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;

关键词: illumination system;    defect detection;    pulse coupled neural network;    particle swarm optimization;    image segmentation;   

DOI  :  10.3390/s110303227

来源: mdpi

【 摘 要 】

Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN) method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO) algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.