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题名: 联合收割机谷物流量检测与数据处理方法研究
其他题名: Research on Grain Flow Measurement and Data Processing Method for Combine Harvester
作者: 王鹤
导师: 胡静涛
关键词: 谷物流量传感器 ; EMD去噪 ; 谷物流量模型 ; 谷物流量实验台 ; 测产系统
页码: 113页
学位专业: 机械电子工程
学位类别: 博士
答辩日期: 2015-11-22
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 信息服务与智能控制技术研究室
中文摘要: 本研究针对精准农业技术体系中产量信息获取这一关键环节,围绕测产技术及应用系统开发,设计了基于PVDF压电薄膜的新型谷物流量传感器,提出了基于END的谷物流量信号去噪方法,建立了基于运动学分析的谷物流量模型,提出了产量误差数据处理方法,设计并开发了谷物流量实验台以及具有扩展性好、通用性强等特点的测产系统。通过初步的实践验证和应用效果分析,本研究成果有较好的实用价值。主要研究结果包括:第一,新型谷物流量传感器的设计。设计了一种基于PVDF压电薄膜的新型谷物流量传感器,介绍了PVDF压电薄膜的特性和传感器的工作原理。根据传感器承载板的动力学模型分析了承载板受力响应对传感器的测量影响,同时指出了承载板阻尼比是提高传感器测量精度的关键,并通过对承载板敷设阻尼材料构成自由阻尼结构来增加其阻尼比。根据变形能原理在阻尼材料一定的情况下自由阻尼结构的阻尼比随着阻尼材料与承载板的厚度比增大而增大。为进一步提高传感器的性能,对承载板和阻尼材料的厚度进行了优化设计。优化后承载板的厚度为1mm,阻尼材料的厚度为6mm。实验结果表明,传感器的最大误差为3.02%,平均误差为2.15%,与其他冲量式谷物流量传感器相比具有测量精度高、结构简单,采用信号处理方法灵活的特点。为谷物流量信号的测量提供一个新的方法。第二,谷物流量信号去噪方法研究。为了消除谷物流量信号中噪声的影响,本文提出了一种基于(EMD)去噪算法。以经验模态分解(EMD)为基础的去噪方法的关键问题是如何确定信噪的分界点。本文推导了高斯白噪声经EMD处理后,各固有模态函数(IMF)之间的能量关系,根据此关系提出了一种基于最小能量准则(MEC)的EMD去噪算法。该算法是对含噪信号进行EMD分解得到IMF分量,然后计算各IMF分量的能量,取具有最小能量的IMF分量作为信噪分界点。仿真验证表明最小能量准则能够准确地判断出信噪的分界点。将本文提出的去噪算法与其他算法对比表明,此算法能够有效地抑制信号的噪声,提高信噪比;在噪声水平未知的情况下,实现了信噪的自动分离,为谷物流量信号的预处理提供了新的方法。第三,谷物流量建模研究。基于运动学原理建立谷物流量模型,该模型综合考虑了谷物流量测量时的外部因素影响,如升运器转速、机械结构参数、相对位置参数等,并且通过引进相关参数来反映谷物外部因素变化影响。结果表明,该模型能够准确描述了冲击力和谷物流量之间的关系,测量的最大误差不超过2.5%,当外部条件变化时能够保持较好的精度,效果要优于常用的经验模型,具有一定通用性和鲁棒性。第四,产量误差数据处理方法研究。研究分析产量数据的获取过程,采用统计分析的原则,将可能造成产量数据误差的归纳为系统误差、随机误差和粗大误差。分别针对不同产量数据的误差来源,提出了误差数据处理方法设计并实现了6种过滤器:割台状态、填充和排空延时、传感器数据异常、不切实际的产量值、局部空间的异常值、短收割段和坐标重叠。过滤后产量数据由随机因素引起的产量数据空间变异得到显著改善。第五,谷物流量实验台及测产系统的设计与实现。为了满足冲量式谷物流量传感器的开发需求, 以常发佳联CF806联合收割机升运器为基础研制了一种谷物流量实验台。三个称重传感器安装在谷物流量实验台的称重箱上,用于标定和精度检验。进给箱的底部设有一块阀板,通过对阀板开度的调节可控制实验过程中不同进给量大小。根据出厂数据和标定实验建立了称重测量模型和进给量模型,并对称重精度和进给量精度进行了分析。室内实验结果表明:谷物流量实验台最大称重相对误差为1.12%,平均相对误差0.61%;进给量在在0.5-2.5 kg/s范围内的相对误差不超过4%。所研制的实验平台运行稳定可靠, 整体性能够满足谷物流量传感器的开发和测试要求。此外,设计开发了基于CAN总线的联合收割机测产系统,实现产量实时检测,并进行了系统整体实验验证。
英文摘要: Yield monitor technology is one of the key technologies in precision farming.In this dissertation.different topics related to yield monitor technology and yield monitor system are studied.A novel grain flow sensor is designed, a grain flow signal denoising method using EMD was proposed, a grain flow model based on kinematics analysis is established, a yield data processing method is put forward, and the grain flow rig and yield system monitor with good expansibility and generality are developed. Through the preliminary practice verification and application effect analysis, the research results had good practical value. The main results are follows: First of all, Design of novel grain flow sensor. A novel grain flow sensor is developed based on PVDF piezoelectric film, which consisted of an impact plate and a PVDF piezoelectric film. The principle of the novel grain flow sensor is introduced. The kinetic model of the novel grain flow sensor is built to analyze that the steady vibration disturbance and the transient vibration disturbance have a significant influence on performance of the sensor, and show that damping ratio of the sensor is the key to improve accuracy of the sensor. A free damping structure to increase damping ratio of the sensor is established by laying damping material on the impact plate. To maximize damping ratio of the sensor, the thickness of the impact plate and damping material are optimized according to a loss factor model of the free damping structure. The optimized results indicated the thickness of the impact plate and damping material were 1mm and 6mm respectively, which make damping ratio of the sensor optimum. Test experiments under different feed flows are conducted on the test rig. Test experiment results show the novel grain flow sensor is good for measuring grain flow with a maximum error of 3.02% and a mean error of 2.15%. Comparing with conventional grain flow sensors, the features of the novel grain flow sensor have a high accuracy, a simple structure and a flexibility which can use signal processing methods. Secondly, study of a grain flow signal denoising method. In order to remove noise from grain flow signal, this dissertation investigated a new method using empirical mode decomposition (EMD).Grain flow signal with noise is decomposed adaptively into intrinsic mode functions (IMFs) that contain specific frequency through sifting. Minimum energy criterion (MEC) is proposed to determine the partial IMFs (low-frequency components) that are used to reconstruct a new grain flow signal. The other IMFs (high-frequency components) are removed as noise. The results indicate that the proposed method have a higher accuracy of grain flow signal measurement. Compared to other reduction noise method, the proposed method can effectively suppress noise improve noise-signal ratio (SNR). The proposed method provides a new way for preprocessing of grain flow signal. Thirdly,modeling of grain flow for grain flow sensor. Currently the grain flow models that were used for grain flow estimation are mainly empirical models. These models are difficult to meet the requirements of robust and versatility. A grain flow model is established based on kinematic analysis to accurately estimate mass flow. The model considered the external factors to grain flow measurement, such as the elevator rotation speed, the mechanical structure parameters, and the relative positions parameters, and introduced relevant parameters that reflected changes of external factors. The relationship between the impact force and grain flow is nonlinear due to the variation of the external factors, a nonlinear least squares method was used to identify the grain characteristic parameters. Experimental results showed that the model can accurately describe the relationship between grain flow and impact, the maximum measurement error do not exceed 2.5%.The model is superior to the empirical models that are usually used, and is with versatility and robustness. Fourthly, study of yield data processing method. Yield data contain systematic and random errors, which must be removed for creating accurate yield maps. Research and analysis of yield data acquisition process, using the principle of hierarchy analysis, the factors that could cause data errors yield factors are summarized as direct and indirect factors and causes. Respectively. To different error sources of yield data, a yield data filtering method is put forward, which filters the six yield data errors: combine header status up, filling and emptying time, sensor outliers, values exceeding minimum and maximum biological yield limits, local neighborhood outliers, and short segments and co-located points. Yield data spatial variability caused by random factors has been significantly improved after using the yield data filtering method. Finally, design of grain flow test rig and yield monitor system. In order to develop a grain flow sensor, a test rig was built. Three weighting sensors were mounted on the weighting bin in the test rig to calibrate grain flow sensor and verify the accuracy of grain flow sensor. A valve plate was inserted in the bottom of the feed bin. The feed flow could be controlled by adjusting opening of the valve plate. A weighting measurement model and a feed flow model were established respectively according to user manual and calibration experiments. Weighting accuracy and feed rate for the test rig was analyzed through experiments. Results showed that the maximum relative error of weighting was 1.12%, the minimum relative error was 0.36%, and the average relative error was 0.61%. The average relative error of feed flow ranging from 0.5 to 2.4 kg/s was less than 4%.The test rig developed is stable and reliable, and meets the requirements for development and testing of the grain flow sensor. In addition, yield monitor system based on CAN bus is developed to achieve yield real-time monitor, and is verificated by field experiment.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/17528
Appears in Collections:信息服务与智能控制技术研究室_学位论文

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