摘要
摘锭作为采棉机采摘棉花的重要工作部件,数量多、服役条件复杂。工作状态中的摘锭和籽棉、棉花秸秆发生直接接触,长时间的摩擦造成摘锭钩齿表面磨损。目前,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。因此,研究一套摘锭磨损程度的自动检测系统,通过数字图像处理来获取摘锭钩齿轮廓的特征信息,利用SPSS软件进行统计分析得到磨损时间与磨损程度的曲线,实现了摘锭磨损程度的自动检测,并确定了摘锭预防性维护的范围,为以后摘锭维护维修及更换提供了理论研究依据。
Picking spindleis an important part of cotton picker. With large number and complicated working conditions, the maintenance of spindle is difficnlt. The spindle directly contacts with cotton seed and straw, which cause the wear of the spindle hook tooth. The degree of spindle wear mainly relies on manual work, which is not only of low efficiency and frequent missed detection, but also of no unified standard, affecting the normal maintenance of the spindle. Based on digital image processing technology, this paper studies the spindle wear degree determination method, with the extraction of characteristic information of spindle hook contour by digital image processing technology. Through statistical analysis of wear time and the wear degree by using the SPSS software, the automatic detection of the wear degree of spindle is realized, and the range of preventive maintenance of ingot is determined, which provides a theoretical basis for the maintenance, repair and replacement of ingot.
作者
吴天松
胡蓉
鲁彦志
WU Tiansong;HU Rong;LU Yanzhi(College of Mechanical and Electronic Engineering, Shihezi University, Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture, Shihezi 832003, Chin)
出处
《机械》
2018年第4期32-37,共6页
Machinery
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(2015ZRKXYQ-LH08)
关键词
采棉机
摘锭
数字图像处理
自动检测
cotton picker
picking spindle
digital image processing
automatic detection
