Design and development of monitoring device for corn grain cleaning loss based on piezoelectric effect
Design and development of monitoring device for corn grain cleaning loss based on piezoelectric effect
复制标题
基于压电效应的玉米籽粒清理损失监测装置的设计与开发
DOI：
10.1016/j.compag.2020.105793
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Fan Yang
中科院分区：
文献类型：
--
作者：
Yanhan Wu;Xiaoyu Li;Enrong Mao;Yuefeng Du;Fan Yang
粮食直收能有效提高玉米收获效率，这是众所周知的。但在粮食直收过程中，不完全脱粒和清选损失会导致玉米籽粒的损失。其中，清选过程中造成的谷物损失可以通过调节清选筛的开度、风机转速等参数来控制和减少。在传统的清选作业过程中，清选机构的调整需要操作人员凭经验进行，这直接导致清选效率低、谷粒损失率高。本文提出的谷物损失率传感器提供了控制上述执行器的反馈参数，使收割机的智能清选成为可能。通过比较现有的研究成果，选择PVDF压电薄膜作为敏感材料。搭建了传感器测试平台和真实的样机测试平台，设计并开发了相应的传感器软件系统。软件系统包括数据采集模块、滤波算法、波形整形模块、计数模块、数据处理模块和无线传输模块。此外，提出了一种算法来校准所造成的错误识别的颗粒和残留物。经过测试和调试，实验室试验的最终辨识误差从12%降到了3%，真实的机试验的误差在6%以内，达到了可以接受的水平，本文的研究成果为谷物收获机械智能控制系统的开发提供了依据，对降低收获机械的清选损失率具有重要意义。
It is wildly known that the grain direct harvest can improve the corn harvest efficiency effectively. However, in the process of grain direct harvest, incomplete threshing and cleaning loss will lead to the loss of corn grain. Among them, the grain loss caused by the cleaning process can be controlled and reduced by adjusting the opening extent of the cleaning sieve, fan speed and other parameters. In traditional operation process, the operator had to adjust the cleaning mechanism based on their experience, which directly leads to low efficiency as well as high rate of grain loss. The grain loss rate sensor proposed in this paper provides the feedback parameters to control the above actuators, which makes the intelligent cleaning of the harvester possible. By comparing the existing research results, the PVDF piezoelectric film was used as the sensitive material. A sensor test platform and a real machine test platform were built and the corresponding software system of the sensor was designed and developed. The software system includes data acquisition module, filtering algorithm, wave shaped module, counting module, data processing module and wireless transmission module. Moreover, an algorithm was proposed to calibrate the error caused by misidentification of the grains and residues. After the testing and debugging, the final identification error of the laboratory tests was decreased from 12% to 3% and the error of the real machine test was under 6%, which is acceptable.The research results of this paper provided the basis for the development of intelligent control system of grain harvesting machinery, and it is of great significance to reduce the cleaning loss rate of harvesting machinery.
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