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Identification of the cleaning process on combine harvesters, Part II: A fuzzy model for prediction of the sieve losses

机译:联合收割机清洁工艺的识别,第二部分:预测筛分损失的模糊模型

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The driving tasks of combine harvester operators can be described as being very exhausting because of the high environmental temperatures, long working days and time constraints operators have to deal with. In order to lighten the job, automation is being introduced. In this study, the focus lies on the automation of the cleaning unit. A non-linear prediction model for the sieve losses has been established by means of fuzzy modelling techniques. Excessive sieve losses can be predicted by making use of a differential pressure measurement under the upper sieve section and the derived sieve load signal. The latter signal is a measure for the loadings by grain and chaff on the upper sieve and can thus be linked with the aeration level of the upper sieve section. Validation results revealed that the best position for mounting a pressure sensor is at the rear section of the upper sieve. Grain loss kernels will be blown out by the fan when the loadings on the upper sieve are very low, while a high loading on the upper sieve will result in sieve overload and thus very high sieve losses. (C) 2009 IAgrE. Published by Elsevier Ltd. All rights reserved.
机译:联合收割机操作员的驾驶任务可谓非常累人,因为环境温度高,工作日长且操作员必须面对时间限制。为了减轻工作量,引入了自动化。在这项研究中,重点在于清洁单元的自动化。已经通过模糊建模技术建立了筛分损失的非线性预测模型。可以通过在上部筛网部分和导出的筛网负载信号下利用压差测量来预测筛网损耗过多。后一个信号是上筛上谷物和谷壳负荷的度量,因此可以与上筛部分的通气水平联系起来。验证结果表明,安装压力传感器的最佳位置是上筛的后部。当上筛网的负荷非常低时,谷物损失的内核会被风扇吹走,而上筛网的高负荷会导致筛网过载,从而造成很高的筛网损失。 (C)2009年。由Elsevier Ltd.出版。保留所有权利。

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