Grain Mass Flow Sensor Calibration via Probability Thresholds
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Solution Overview
Problem
Existing mass flow measuring devices in harvesting machines face challenges in accurately measuring grain flow due to variations in crop moisture and operational conditions, leading to inconsistent calibration and reduced accuracy.
Innovation Solution
A method that determines a change in mass of grain in a grain tank over time using sensor signals, evaluates the accuracy of this change through a decisioning algorithm, and adjusts the calibration factor for the mass flow sensor only if the probability value exceeds a certain threshold, while considering the geometric shape of the grain pile and ensuring the sensed mass flow rate falls within predetermined limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If calibration is performed continuously using mass flow sensor signals, then calibration coverage is improved, but measurement accuracy deteriorates due to unreliable data under varying operational conditions
Solution Approach 1:
The calibration process dynamically adjusts based on operational conditions. The system evaluates probability values in real-time to determine whether calibration data is reliable, making the calibration process adaptive rather than static. This resolves the contradiction by enabling continuous calibration monitoring while filtering out unreliable data points that would degrade accuracy.
Solution Approach 2:
The system implements feedback through probability evaluation of calibration data. Before accepting calibration data, the system evaluates whether operational conditions meet reliability thresholds. This feedback mechanism prevents inaccurate calibration under poor conditions while maintaining calibration progress under favorable conditions, thus improving both coverage and accuracy.
2Quantity of substance
If calibration data is accepted from all operational conditions, then calibration completeness is improved, but data reliability deteriorates due to varying crop moisture and operational factors
Solution Approach 1:
The system applies different quality standards to different calibration data based on local operational conditions. Rather than uniformly accepting or rejecting all calibration data, the system evaluates each data set against probability thresholds specific to the operational conditions at that moment. This allows high-quality data to be accepted while filtering out unreliable data from adverse conditions.
Solution Approach 2:
The system changes the acceptance parameters for calibration data based on operational conditions. Probability thresholds and evaluation criteria are adjusted according to crop moisture, flow rate, and other operational factors. This dynamic parameter adjustment enables the system to maintain high data reliability while still accumulating sufficient calibration data across varying conditions.
3Reliability
If calibration is suspended during acceleration or deceleration, then measurement reliability is improved, but calibration time increases
Solution Approach 1:
The calibration process dynamically responds to operational conditions such as acceleration and deceleration. Rather than statically suspending calibration during these events, the system evaluates probability values to determine if calibration can proceed reliably. This dynamic approach minimizes unnecessary calibration suspensions while maintaining reliability standards.
Solution Approach 2:
The system performs preliminary evaluation of operational conditions before accepting calibration data. By evaluating probability values in advance, the system can identify suitable calibration opportunities during transient operations without compromising reliability. This preliminary assessment reduces calibration time by capturing valid data during events that would traditionally be excluded.
Data Source
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AI summary
A method of calibrating a mass flow sensor while harvesting grain includes sensing an accumulated mass of a portion of grain within the grain tank with a first sensor. A mass flow rate sensor is calibrated based at least in part on a signal of the first sensor.