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Improved piezoelectric grain cleaning loss sensor based on adaptive neuro-fuzzy inference system
Precision Agriculture ( IF 6.2 ) Pub Date : 2022-01-29 , DOI: 10.1007/s11119-022-09879-2
Mingzhi Jin 1 , Zhan Zhao 1 , Shuren Chen 1 , Junyi Chen 1
Affiliation  

Grain cleaning loss rate is an important performance index of combine harvesters which needs to be measured in real time during the harvesting operation. To improve the measurement accuracy and range, a grain loss sensor based on piezoelectric effect and adaptive neuro-fuzzy inference system (ANFIS) was proposed. A piezoelectric ceramic was fixed on the bottom of a thin sensitive plate to detect grain impact, and the sensitive plate was fixed to a support plate with a piece of shock-absorbing rubber between them to increase the attenuation rate of the vibration generated by grain impact. Based on the analysis of the reasons that restrict the improvement of measurement performance of traditional measurement methods, a novel signal processing circuit was designed. The circuit could simultaneously measure the number and energy of grain impacts, and output the results in the form of square wave voltage and analog voltage, respectively. Variation characteristics of the two output signals under different grain impact frequencies were analyzed. Then, a grain impact frequency prediction method based on ANFIS fusion of the two signals was proposed, and the established ANFIS model was trained through the calibration tests. Finally, measurement tests were carried out, and the results indicated that the measurement errors of grain impact were less than 2.5, 3.9, 4.4, 6.5 and 9.2% with measurement ranges of 100, 200, 600, 1000 and 1500 grain/s, respectively. With increase of MOG/grain mass ratio, the measurement error of the sensor was increased gradually due to the collision interference between MOG and grain. Compared with traditional sensors, the measurement accuracy and range were both improved significantly.



中文翻译:

基于自适应神经模糊推理系统的改进型压电晶粒清洗损耗传感器

粮食清理损失率是联合收割机的一项重要性能指标,需要在收割作业过程中实时测量。为了提高测量精度和测量范围,提出了一种基于压电效应和自适应神经模糊推理系统(ANFIS)的谷物损失传感器。将压电陶瓷固定在薄敏感板底部以检测谷物冲击,将敏感板固定在支撑板上,两者之间有一块减震橡胶,以增加谷物冲击产生的振动的衰减率. 在分析制约传统测量方法提高测量性能的原因的基础上,设计了一种新型的信号处理电路。该电路可以同时测量谷物撞击的次数和能量,并分别以方波电压和模拟电压的形式输出结果。分析了两种输出信号在不同颗粒冲击频率下的变化特征。然后,提出了一种基于两信号ANFIS融合的粮食冲击频率预测方法,并通过标定测试对建立的ANFIS模型进行训练。最后进行了测量测试,结果表明,在100、200、600、1000和1500粒/s的测量范围内,颗粒冲击的测量误差分别小于2.5、3.9、4.4、6.5和9.2% . 随着MOG/颗粒质量比的增加,由于MOG与颗粒之间的碰撞干扰,传感器的测量误差逐渐增大。与传统传感器相比,

更新日期:2022-01-30
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