Improved optical-type measurement method of grain flow using array near-infrared photoelectric sensors

Abstract

To improve the optical-type measurement accuracy of the grain flow in a flight elevator, it is important to analyze the effect of grain accumulation shape on the mass. In this paper, a method for measuring grain accumulation thickness using a near-infrared photoelectric sensor was developed. According to the attenuation characteristics of light intensity in the discontinuous medium, a Gaussian regression model was established to describe the relationship between the output voltage and the grain accumulation thickness. Calibration results indicated that the measurement error was less than 0.5 mm with the infrared wavelength of 940 nm. Then, three sensors were arranged in parallel as an array to measure the change process of grain accumulation thickness on the flight during the lifting process. A back propagation (BP) neural network which takes time series of the output voltage as inputs was developed to predict the grain mass. The measurement experiments were carried out on a single flight elevator test-rig. After training using the experimental data, the measurement error and the average error using the BP neural network were less than ± 18.2 g and 7.6 g with the grain mass in the range of 0 – 600 g. Finally, grain flow measurement tests were carried out on a grain flow test-rig, and the results indicated that the relative error reduced from 5.6 to 1.4% when the grain flow increased from 0.5 to 2.8 kg/s. Compared with the traditional method, the accuracy of the proposed method was significantly improved.