Self-calibrating mass flow sensor

Flow sensor;impact plate;precision farming;nonlinear regression;discrete element modeling;combine

Reinke, Ryan E. ; Dankowicz ; Harry

关键词: Flow sensor;    impact plate;    precision farming;    nonlinear regression;    discrete element modeling;    combine;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/27750/Reinke_Ryan.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

A comprehensive analysis was conducted for increased accuracy and self-calibration for a mass flow sensing system on a combine. This was undertaken as part of the John Deere Technology InnovationCenter (JDTIC)-sponsored research program “Self-calibrating mass-flow sensor”, in turn part of a John Deere Moline Technology Innovation Center (MTIC) effort toward optimization and closer integration of the components of the mass-flow sensing system in Deere harvesting combines. The long-term objective was to achieve a self-calibrating sensor system capable of adapting to varying input conditions due, forexample, to changes in grain moisture content and aging of the system’s elevator paddles.In analyzing the mass flow sensing system, a physics-based model was developed to describe therelationship between the rate of mass flow through the combine and the measured force imparted to theimpact plate in terms of mechanical properties of the grains and the interior geometry of the combine. Acomputational realization of this model was constructed in Matlab. Accurate mass flow rate estimationwas achieved through model-based estimation based on nonlinear regression applied to the physics-basedmodel and data acquired through simulation and experimentation. Model-based estimation was alsoextended as a means for self-calibration of the sensing system. Through development of the physics basedmodel, the dependence of the force imparted to the impact plate on the orientation of the impactplate was identified. By inducing known changes to the impact plate orientation and implementingmodel-based estimation, a means of self-calibration of the sensing system was achieved.Three methods of model-based estimation were successfully demonstrated using data generated from the physics-based model. Additionally, these were further verified using data collected from discrete element modeling simulations, and experimental data collected in two fashions: using a full-scale replica of the mass flow sensing system, and using a small-scale, benchtop testing apparatus. Furthermore, the ability of the developed algorithm to update theoretical model parameters while simultaneouslyestimating mass flow rate was shown to enable the system to self-calibrate. This was argued to allow the system to accommodate different operating conditions that may be encountered during combine harvesting, such as changes in crop moisture, grain variety, and aging of combine components.