Grain Mass Flow Sensor Calibration Compensation
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Solution Overview
Problem
Current grain mass flow sensor calibration systems face inaccuracies due to variable grain properties such as moisture, density, kernel size, and frictional characteristics, which are affected by dimensional variations in harvester components and harvesting conditions, making it impractical to determine highly accurate pre-determined calibration curves.
Innovation Solution
A yield monitor system that determines calibration curve variation coefficients based on instantaneous or periodic measurements of grain parameters, using intelligent control to adjust the mass flow calibration curve, incorporating strain gauges and data from hauling vehicles to minimize mass variations and account for grain moisture, density, and other properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-determined calibration curves are used for mass flow measurement, then the system is simple to operate, but accuracy deteriorates due to variations in grain properties and harvester component dimensions
Solution Approach 1:
The calibration curve is transformed from a static, pre-determined value into a dynamic, adjustable parameter that adapts to changing grain properties and harvester conditions. The system continuously modifies the calibration curve based on real-time measurements of grain moisture, density, and mass flow rate, allowing the calibration to evolve with operating conditions rather than remaining fixed.
Solution Approach 2:
The system changes the parameters of the calibration curve (slope and intercept) based on measured grain properties such as moisture content and density. By adjusting these parameters in real-time according to actual grain characteristics, the system maintains high measurement accuracy across varying conditions without requiring complex pre-determination for all possible scenarios.
2Measurement precision
If calibration curves are adjusted for different grain moisture levels, then measurement accuracy improves, but system complexity increases due to multiple calibration curves and interpolation requirements
Solution Approach 1:
A single universal calibration curve structure is used that can adapt to all grain moisture levels and conditions through parameter adjustment, rather than requiring separate calibration curves for each moisture level. This universal approach simplifies the system while maintaining accuracy across the full range of operating conditions.
Solution Approach 2:
The system uses real-time feedback from grain moisture sensors and mass flow measurements to automatically adjust calibration parameters. This closed-loop feedback mechanism eliminates the need for manual selection or complex interpolation between multiple pre-determined curves, as the system automatically adapts to current conditions.
3Measurement precision
If dimensional variations in harvester components are accounted for in calibration, then measurement accuracy improves, but the difficulty of determining calibration curves increases due to numerous component variations
Solution Approach 1:
The system performs self-calibration by using its own measurements of mass flow rate and grain properties to automatically determine the appropriate calibration parameters for its specific configuration. Rather than requiring external calibration procedures for each component variation, the system adapts to its own characteristics through operational data.
Solution Approach 2:
Physical measurement and adjustment procedures are replaced with electronic sensing and computational adjustment. The system uses sensors to detect grain properties and computational algorithms to determine calibration parameters, eliminating the need for manual mechanical calibration procedures for each harvester configuration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of grain mass flow measurement by dynamically adjusting the calibration curve to compensate for varying grain properties, improving the precision of yield monitoring systems and reducing the need for extensive pre-determined calibration curves.
Implementation Method 1
a grain mass flow sensor which includes an impact plate and strain gauges attached to the impact plate
Data Source
AI summary
A yield monitor system is configured to determine how the calibration characteristics of a grain mass flow sensor on an individual combine are affected by grain moisture content and/or other grain parameters which can be measured instantaneously or periodically by the yield monitor system or its operator, or which can be observed by the operator, or which can be determined from other reference information, such as maps of where different grain varieties or hybrids were planted. Other systems, methods, and apparatuses are also provided.