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首页> 外文OA文献 >A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part II:Selection of relevant input variables for identification of material other than grain (MOG) content in the grain bin
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A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part II:Selection of relevant input variables for identification of material other than grain (MOG) content in the grain bin

机译：用于识别联合收割机上清洁过程的遗传输入选择方法，第二部分：选择相关输入变量以识别谷物仓中除谷物（MOG）以外的物质

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摘要

The cleaning process on a combine harvester is a complex process that is influenced by awide range of parameters such as machine settings, field and crop-related parameters, etc.Because of the high time pressures combine drivers have to deal with, optimal settings forthe cleaning section are usually only estimated once for each crop. As a consequence,differences in temporal and site-specific conditions are neglected. No recent literature isavailable that considers the interaction between the settings of the cleaning section (likee.g. fan speed, lower sieve opening and upper sieve opening) and the material other thangrain (MOG) content in the grain bin, which is, however, an important performanceparameter of the cleaning shoe.In this study, a combine harvester was equipped with extra sensors that could containvaluable information necessary to predict the performance of the cleaning section. A nonlineargenetic polynomial regression technique was used to rank the pool of potentialsensors as possible regression variables for a prediction model of the MOG content in thegrain bin. This model is important for the automation of the cleaning shoe. Results showedthat the MOG content in the grain bin is influenced non-linearly by differences in theamount of biomass on the sieve section and the fan speed, which are also correlated witheach other.
机译：联合收割机的清洁过程是一个复杂的过程，受各种参数的影响，例如机器设置，田地和与作物相关的参数等。由于联合收割机驾驶员需要处理高时间压力，因此清洁的最佳设置通常对每种作物只估计一次。结果，忽略了时间和特定地点条件的差异。没有最新的文献考虑清洁部分的设置（例如风扇速度，下部筛孔和上部筛孔）与谷物仓中除谷物（MOG）含量以外的物质之间的相互作用，但是，在这项研究中，联合收割机配备了额外的传感器，这些传感器可能包含预测清洁部分性能所需的有价值的信息。使用非线性遗传多项式回归技术对潜在传感器池进行排序，以作为谷物仓中MOG含量预测模型的可能回归变量。该模型对于清洁靴的自动化非常重要。结果表明，粮仓中MOG的含量受筛段生物量和风机转速的差异非线性影响，二者之间也存在相关性。

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