摘要
目前,采棉机摘锭磨损程度主要依靠人工判定,这种方法不仅效率低、易漏检,而且没有统一的标准,影响了摘锭正常的维护维修,为了快速准确地检测出摘锭磨损程度,本文提出了基于机器视觉的摘锭磨损程度定量判定方法,首先根据实际需要搭建了适宜采集摘锭钩齿轮廓的采集系统,确定采集系统的各个硬件设备和软件部分。然后利用该采集系统进行摘锭数字图像采集,利用图像处理软件提取摘锭磨损程度特征信息,最后利用图像处理软件来统计摘锭磨损图像的像素值,计算出它们的像素数差,也即面积差值ΔS,ΔS求取的大小就表示摘锭的磨损程度。实现摘锭磨损程度的表达可以为以后的摘锭维护维修和更换等提供大量的数据参考。
At present, the spindle wear degree of cotton picker mainly rely on the artificial method, yet this method is low effi- ciency and easy to miss, furthermore, there is no uniform standard so that the normal maintenance of spindle would be influ- enced. In order to quickly and accurately detect the spindle wear degree, the spindle wear degree determination method based on the machine vision is proposed in this paper. According to the actual needs, the acquisition system for collecting the spin- dle hook contour is established, and the hardware and software parts of the acquisition system are determined. Then the digital image of spindle is collected by using the acquisition system, and the feature information of spindle wear degree is extracted by using the image processing software. Finally the image processing software is used to count the pixel value of the spindle wear image, so as to calculate the difference of their pixel count, namely the area difference, and the calculated result stands for the wear degree of the picking ingot. The expression of the wear degree of the spindle can provide a large number of data reference for the maintenance and replacement of the spindle.
出处
《机械研究与应用》
2017年第6期159-162,共4页
Mechanical Research & Application
基金
石河子大学校级应用基础研究项目:采棉机MRO系统的摘锭维护决策研究(编号:2015ZRKXYQ-LH08)
关键词
采棉机
摘锭
数字图像
采集系统
磨损程度
cotton picker
spindle picking
digital image processing
acquisition system
wear degree
