收割机脱粒滚筒负荷多目标优化模型研究——基于排队网络和遗传算法

Study on Multi-objective Optimization of Threshing Drum Load——Based on Queuing Network and Genetic Algorithm

为了提高收割机脱粒滚筒的自动化排障水平,实现收割机滚筒的自动化监测功能,提出了脱粒滚筒负荷监控系统的设计方案,实现了脱粒滚筒堵塞故障的预警、报警及自动防堵功能。该系统使用传感器对凹板压力、传动链张紧力和滚筒转速进行检测,并使用上位机对监测的信息进行数据处理,利用排队网络和多目标遗传算法对负荷参数进行优化,将优化后的负荷作为调整参数输出到控制器,调整脱粒间隙的大小,实现脱粒滚筒的智能化排堵,从而实现不停机排障,提高了联合收割机作业质量和工作效率。由滚筒的脱净率实验发现:脱净率最高的是排队网络遗传多目标优化算法。由此验证了所设计的脱粒滚筒负荷优化控制模型的可靠性。

In order to improve automation of the row barrier in harvester threshing cylinder,drum harvester automation monitoring function,it puts forward the design scheme of load system of threshing cylinder,the threshing drum jam fault early warning,alarm and automatic blocking function. The system uses sensor to detect the concave pressure plate,chain drive tightening force and roller speed,and uses the host computer of monitoring data processing,uses queuing network and multi- objective genetic algorithm to optimize the parameters of load,the optimized load as the adjustment of the reference number of output to the controller,threshing clearance adjustment of size,intelligent threshing cylinder row blocking,and without stopping the machine troubleshooting,and to improve the quality and efficiency of combine harvester. From the drum to the off net rate experiment,it found that the off net rate is the highest in the queuing network genetic multi- objective optimization algorithm,which verifies the design of the optimal control model of reliability of the threshing roller load in this paper.