为了分析籽粒损失监测传感器敏感板结构对籽粒碰撞信号的影响，该文通过 ANSYS 软件对籽粒损失监测传感器不同结构形式的敏感板进行模态分析，研究了敏感板振动特性与籽粒损失监测传感器检测性能之间的关系，并在实验室内进行了籽粒碰撞试验.试验结果表明，一阶固有频率 p 越高，信号衰减时间 t 越短；相对变形率越大，籽粒损失监测传感器整体灵敏度越高；在敏感板长度 l=150 mm、宽度 b=40 mm、厚度 h=1.0 mm 时籽粒损失监测传感器的检测频率和整体灵敏度较高；以20~120粒/s 的籽粒流量对此结构形式下的籽粒损失监测传感器进行检测误差试验，最大检测误差为2.7%.在自制的标定试验台上利用饱满水稻籽粒、不饱满水稻籽粒、不同长度茎秆组成的混合物料对该籽粒损失监测传感器进行标定，结果表明，该籽粒损失监测传感器能从混合物料中有效地识别出饱满籽粒，最大检测误差为2.3%，该文的研究对提高籽粒损失监测传感器的检测频率和测量精度具有重要意义.%The ratio of the separation loss was an important indicator to measure the operating performance of combine harvester, and it also was an important criteria to adjust the relevant operating parameters. Traditional separation loss detecting methods mainly rely on a manual approach. An oil skin was used to collect all the mixed material at the exhaust port, then it filtered out the grain from material-other-than-grain (MOG) manually, was weighed and calculated out the separation loss. It was apparent that such a heavy workload, high labor intensive, time-consuming method could not meet the developing trend of the combine harvester. With the recent advances in sensors, electronics and computational processing power, automated technologies for combine harvesters have been made possible in part and there is an urgent need to develop a system which could monitor the separation loss in real-time. Relevant research indicated that the structural form of the grain loss monitoring sensor had a strong influence on performance of the grain loss monitoring sensor. In order to analyze the impact of sensitive plate structure on detecting the performance of grain loss monitoring sensors, modal analysis were carried out though the ANSYS software, and the rice grain impact tests were carried out on different structural forms of a sensitive plate. The results showed that the higher the first natural frequency p, the shorter the signal attenuation time t; the higher relative deformation γrate, the higher the overall sensitivity. Selected high-sensitivity receiver materials such as piezoelectric ceramic YT-5 as sensitive components, a signal process circuit which was composed of voltage amplifier, aband-pass filter, precision full-wave rectification, an envelope detector to measure the grain impact signal and a secondary instrument which used AT89C52 microcontroller as the core chip were developed to acquire the grain impact signal. Critical frequencies of band-pass filters were set to 5-20 kHz, with a rice grain with a quality of 29.3 mg, and caused to fall from a distance of 350mm high to collide with the sensitive plate of grain loss monitoring sensors. The sensors recorded the signals after they were processed by a charge amplifier and the band-pass filter with a storage digital oscilloscope DS01022A and the sampling frequency was set to 100 kHz. It was found that when the sensitive plate length l=150 mm, width b=40 mm, thickness h=1.0 mm, the detection frequency and overall sensitivity of the sensor were relatively high compared with other structures. Calibration experiments were carried out on the calibration test-bench indoor, which was composed of a lifting platform, lifting driving mechanism, feeding device and the sensor installation platform to test the detecting accuracy of the sensor. The results showed that the maximum detection error was 2.7% when a grain flow rate within 20 to 120 grains per second on condition of the sensitive plate length l = 150mm, width b =40mm, thickness h=1.0 mm, mounted the sensor on the calibration test-bench with a angle of 450, and the material fell from a height of 200 mm. Each calibration test was repeated three times. In order to test the ability of grain loss monitoring sensor h with sensitive plate length l=150 mm, width b=40 mm and thickness h=1.0 mm to detect out the full rice grains from strong interference, full rice grains(1000 grains), blighted rice grains(100 grains, weight 2g) with moisture content of 24.58% and stalks with different lengths (15-20, 50-60 mm, and weight 10 g respectively) with moisture content of 66.52% have been selected as calibration materials to test the performance of the sensor. Results showed that the sensor could discriminate full rice grains effectively. The detection error rate was less than 2.3%.
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