摘要
为了对比不同喂入量检测方法的性能,并明确不同作业状态参数对联合收割机喂入量的表征程度,设计了基于割台传动轴扭矩、脱粒滚筒轴扭矩、过桥底板压力、割台传动轴转速、过桥轴转速及脱离滚筒轴转速这6个变量的喂入量间接检测系统。以南粳9108为试验对象,以中联重科4LZT-6.0ZE型联合收割机为试验平台,进行了系统试验,并通过数据采集卡采集各传感器输出,对输出数据进行了性能分析及主成分分析。试验及分析结果表明:扭矩系统整体性能优于压力系统,割台主动轴扭矩、脱粒滚筒轴扭矩、过桥底板压力及三轴转速对喂入量变化的贡献率达85%以上,且脱粒滚筒轴扭矩对于喂入量变化的表征程度最高。研究结果为联合收割机喂入量检测方法优化提供了参考。
In order to compare the performance of different feeding rate detection methods,and to clarify the characterization degree of the feed rate of the combine harvester by the parameters of different operating conditions,an indirect detection system for the feeding rate was designed,which based on six variables the header drive shaft torque,the threshing drum shaft torque,the bridge floor pressure,the header drive shaft speed,the bridge shaft speed,and the off-drum shaft speed.A system test was carried out with the rice Nanjing 9108 being taken as the test object,and the Zoomlion 4LZT-6.0ZE combine harvester taken as the test platform.In the test,the output of each sensor was collected by the data acquisition card,and the performance analysis and principal component analysis of the output data were carried out.The test and analysis results show that the overall performance of the torque system is better than that of the pressure system.The main shaft torque of the header,the torque of the threshing drum and the pressure of the bridge bottom plate contribute more than 50%to the change in the feed rate.Among them,the torque of the threshing drum is important for feeding.The characterization degree of the change in the input amount is the highest,and the research results provide a reference for the optimization of the detection method of the combine harvester’s feeding amount.
作者
汤玲玉
魏新华
童浩
李川
吴抒航
Tang Lingyu;Wei Xinhua;Tong Hao;Li Chuan;Wu Shuhang(College of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,China)
出处
《农机化研究》
北大核心
2023年第5期37-42,共6页
Journal of Agricultural Mechanization Research
基金
江苏省科技项目(BE2020327)。
关键词
联合收割机
喂入量检测
数据采集
主成分分析
combine harvester
feeding rate detection
data acquisition
principal component analysis