Design and Test of Sensor for Monitoring Corn Cleaning Loss

At present, Chinese corn grain harvesters lack cleaning loss monitoring. Cleaning parameters cannot be automatically adjusted, and the loss rate is high. In view of the above problems, a cleaning loss monitoring sensor is designed, composed of a metal impact plate, piezoelectric ceramic and signal processing circuit. The factors affecting the characteristics of vibration signals are analyzed from the material, size and other aspects. The sensitive plate is composed of a 304 stainless steel impact plate and piezoelectric ceramic. The sensitive plate can convert the vibration signal of the impact plate into a voltage signal, and the output voltage range can reach ±3 V or more. The signal generated by the collision of corn kernel and damaged corn cob with the sensitive plate was analyzed.It was found that the frequency domain range of corn grains was wider, with signals above 6 kHz, but broken corncobs did not have such signals. Based on the frequency distribution, a signal processing circuit is designed, which consists of high-pass filter circuit, an envelope detection circuit, and a voltage comparison circuit. The circuit can convert analog signals into pulse signals, which facilitates the counting process by the microprocessor. In order to obtain the monitoring accuracy and installation parameters of the integrated corn cleaning loss monitoring sensor, a Central Composite Design was carried out with the installation height and angle of the sensitive plate as the test factors and monitoring accuracy as the test index. Based on the test results and field test conditions, a regression model was established to determine the optimal installation parameters: the installation angle of the sensitive plate is 30°, and the installation height is 30 cm. At this stage, the accuracy of the sensor monitoring corn grains was 92.82%, and the accuracy of monitoring the mixture of corn grains and broken corncobs was 90.07%. The verification test shows that the monitoring accuracy can reach more than 94% after the sensor is debugged. This research can provide a reference for the design of corn cleaning loss monitoring devices.