基于油压力的联合收获机脱粒滚筒喂入量模型试验研究

项目名称： 基于油压力的联合收获机脱粒滚筒喂入量模型试验研究

项目编号： No.61004085

项目类型： 青年科学基金项目

立项/批准年度： 2011

项目学科： 金属学与金属工艺

项目作者： 卢文涛

作者单位： 洛阳理工学院

项目金额： 7万元

中文摘要： 喂入量是谷物联合收获机设计脱粒系统最重要的参数之一，且喂入量很难直接测量，因而如何准确获取喂入量是目前研究方向之一。本项目将驱动脱粒滚筒液压无级变速系统的油压力纳入喂入量模型，以脱粒滚筒为研究对象，对脱粒力矩的形成进行研究，得出喂入量与油压力之间的理论关系方程；对谷物湿度、谷草比的变化对油压力的影响进行了探索，获得了油压力分别随这些参数的变化规律。台架喂入试验结果表明：作物物理特性一致时，秸秆喂入量不变，油压力为常数；当喂入量小于2.5kg/s时，油压力随着喂入量提高而增加缓慢，当喂入量达到2.5kg/s后，油压力随着喂入量提高呈线性增长；当喂入量、谷物湿度等参数不变时，油压力随谷草比的增加而增加；在喂入量等其他条件不变时，油压力随谷物湿度的增加先增大后减小，在谷物湿度为30%~35%时油压力取得最大值。 将油压力、谷草比、谷物湿度作为输入，喂入量作为输出，建立神经网络模型。该模型的应用可以及时准确对喂入量进行检测，实现收获机自动控制系统的建立，从而降低驾驶员的劳动强度，提高作业质量，为降低谷物损失率及机械故障率，提高收获效益奠定基础。

中文关键词： 联合收获机；喂入量；油压力；谷物湿度。

英文摘要： The feed rate was one of the most important parameters of threshing system of combine harvester and was difficult to be detected directly, and so how to obtain the feed rate was one field of the current studies. The hydraulic oil pressure of threshing cylinder continuously variable transmission system was adopted into the feed rate model, and then the relation equation between feed rate and oil press was obtained by considering the threshing cylinder as object of study and studying on the formation of threshing torque; the effect of crop moisture content, grain-straw ratio on the hydraulic oil pressure were explored to obtaining the variation rule of oil pressure with these parameters respectively. Experiment results showed that the oil pressure could be considered as a constant when crop properties were in the same and straw feed rate remained unchanged, and oil pressure increased slowly when feed rate was less than 2.5kg/s, but oil press was linear with the feed rate when feed rate was more than 2.5kg/s; when feed rate, crop moisture content and the other parameters remained unchanged, the oil pressure increased as the grain-straw ratio advanced; when the feed rate and other parameters remain unchanged, the oil pressure increased first and then decreased as the crop moisture content increased, and the oil pressure had maximum when crop moisture content was between 30%~35%. The neural network model was established by using oil pressure, crop moisture content, grain-straw ratio as inputs and feed rate as the output. With the application of this model, the feed rate will be detected timely and accurately, and the automatic control implementation of feed rate will be possible, then the intensity of labor of the driver will be decreased, and the harvesting quality will be improved, which established the foundation of decreasing grain loss and mechanical fault rate and improving harvesting benefit.

英文关键词： Combine harvester; Feed rate; Hydraulic oil pressure; Crop moisture content