采棉机摘锭磨损的自动检测及摘锭寿命预测
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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 difficult. 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.
Picking spindleis an important part of cotton picker. With large number and complicated working conditions, the maintenance of spindle is difficult. 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.
引文
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[11]鲁彦志,胡蓉,赵家伟.采棉机摘锭锥齿轮副疲劳寿命预测仿真分析[J].机械,2017,44(2):5-9.
[12]徐涛,祁学潮,张六泉.超音速火焰喷涂WC-10Co-4Cr和Co Cr W涂层的微动磨损特性研究[J].机械,2016,43(9):39-44.
[2]何翔,任小洪.基于数字图像的刀具磨损状态检测技术[J].机床与液压,2016,44(3):125-128.
[3]高岚,占健强,卢凌.基于小波变换的气缸内表面磨损检测的研究[J].武汉理工大学学报(交通科学与工程版),2003,27(3):330-332,336.
[4]石桂名,魏庆涛,孟繁盛.基于Canny算子的图像边缘检测算法[J].现代电子技术,2015,38(12):92-93,97.
[5]黄先北,杨硕,刘竹青,等.基于颗粒轨道模型的离心泵叶轮泥沙磨损数值预测[J].农业机械学报,2016,47(8):35-41.
[6]于劲松,时祎瑜,梁爽,等.基于狄利克雷混合模型的刀具磨损量在线估计[J].仪器仪表学报,2017,38(3):689-69
[7]丁大尉,纪淑波,王立志.光电式钢轨磨损实时检测系统[J].中国铁道科学,2006,27(1):64-67.
[8]倪洁,王俊平,杨国钰,等.一种柔性形态学参数对称对数图像处理新算法[J].西安电子科技大学学报,2017,44(5):26-32.
[9]王正直,辜萍,杨翀,等.基于MATLAB图像处理技术计算微纳米级磨损量[J].实验力学,2009,24(5):389-394.
[10]张自超,王福军,陈鑫,等.基于改进欧拉算法的双吸离心泵泥沙磨损特性研究[J].农业机械学报,2017,48(3):124-133,147.
[11]鲁彦志,胡蓉,赵家伟.采棉机摘锭锥齿轮副疲劳寿命预测仿真分析[J].机械,2017,44(2):5-9.
[12]徐涛,祁学潮,张六泉.超音速火焰喷涂WC-10Co-4Cr和Co Cr W涂层的微动磨损特性研究[J].机械,2016,43(9):39-44.