基于机器视觉的推力轴承垫圈缺陷检测系统研究

Research of defect detection system for thrust bearing gasket based on machine vision

为实现推力轴承垫圈表面缺陷自动检测,解决检测过程人工效率低、准确性波动大的问题,设计一套推力轴承垫圈表面缺陷检测及分类系统。针对垫圈表面图像背景复杂,干扰较多难以提取缺陷等问题,用最小二乘法圆拟合提取圆环形感兴趣区域,通过傅里叶变换、低通滤波器和傅里叶逆变换对图像卷积滤波从而抑制干扰;然后基于多个形状指标和多个阈值提出缺陷提取分类算法,从而达到检测缺陷和分类的目标。通过实验并分析结果:对单个垫圈检测时间为1064.89 ms,垫圈端面缺陷检测准确率在95.23%以上,满足实际检测要求,有具体使用价值,可为自动化检测推力轴承垫圈的缺陷提供新的方法。

In order to achieve the automatic detection surface defects of thrust bearing gasket,solve the problems of low artificial efficiency and large fluctuation of accuracy in the detection process.A surface defects detection and classification system of thrust bearing gaskets is designed.In view of the complex background of the image on the gasket surface and the difficulty in extracting defects due to too much interference,the least square method is used to extract the circular region of interest,and the image convolution was filtered by the Fourier transform,low pass filter and inverse Fourier transform to suppress the interference.Then a defect extraction and classification algorithm based on multiple shape indexes and multiple threshold is proposed,to achieve the goal of defect detection and classification.The results of the experiment and analysis show that the detection time of a single gasket is 1064.89 ms,the detection accuracy of the end surface defect of the gasket is more than 95.23%,which can meet the actual detection requirements.It has specific application value and provides a new method for automatic detection of the defects of the thrust bearing gasket.