Weighing Platform Eccentric Load Correction Using Threshold Feedback
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Solution Overview
Problem
Existing methods for eccentric load error correction in weighing devices, particularly multi-sensor systems, fail to accurately minimize errors due to non-normal distribution of weighing data sets, leading to inaccurate correction coefficients.
Innovation Solution
A method and system that calculates sensor correction coefficients based on differences exceeding a pre-set threshold, using a linear parameter and convergence rate, ensuring accurate correction by updating data sets until differences are within the threshold range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the minimum sum of squared differences approach is used to calculate correction coefficients, then the calculation complexity is reduced, but the measurement precision deteriorates because weighing data sets do not conform to normal distribution
Solution Approach 1:
The patent changes the statistical parameter assumption from normal distribution to non-normal distribution, and modifies the correction coefficient calculation from minimum sum of squared differences to a threshold-based iterative approach. This allows the system to adapt to the actual statistical characteristics of weighing data while maintaining calculation feasibility.
Solution Approach 2:
The patent introduces a feedback mechanism where correction coefficients are iteratively adjusted based on whether the absolute difference between weighing data and average value exceeds a preset threshold. The process continues until convergence criteria are met, ensuring continuous improvement of correction accuracy.
2Ease of operation
If correction coefficients are adjusted using existing methods, then the correction process is simplified, but the reliability of eccentric load error correction deteriorates due to non-normal distribution of weighing data
Solution Approach 1:
The patent implements an iterative feedback process where correction coefficients are continuously refined based on threshold comparisons. The system checks whether absolute differences exceed the preset threshold and adjusts coefficients accordingly, repeating until convergence. This feedback mechanism ensures reliable correction despite non-normal data distribution.
Solution Approach 2:
The patent transforms the static correction coefficient adjustment into a dynamic iterative process. The correction coefficients are not fixed but are continuously updated based on the current state of weighing data, allowing the system to adapt to varying conditions and achieve reliable correction.
3Measurement precision
If threshold-based iterative correction is implemented, then the measurement precision is improved, but the productivity decreases due to multiple iterations required for convergence
Solution Approach 1:
The patent applies partial action by only correcting data points where the absolute difference exceeds the preset threshold, rather than processing all data points uniformly. This selective approach focuses computational resources on the most significant errors, improving precision while reducing overall computational burden.
Solution Approach 2:
The patent performs preliminary calculations of average values and threshold comparisons before full correction coefficient adjustment. This preliminary action identifies which data points require correction, allowing the iterative process to focus only on necessary adjustments and improving overall efficiency.
Data Source
AI summary
Methods and systems for eccentric load error correction are disclosed. A plurality of weighing data sets for a weight having a mass value are obtained, where the weight is loaded at different positions on a weighing platform of a weighing device. Differences between each of the weighing data sets and the average value of the plurality of weighing data sets or the mass value of the weight are calculated. Sensor correction coefficients are calculated and updated when the maximum absolute value of the differences exceeds a pre-set threshold. The weighing data sets are updated. The above steps are repeated until the absolute values of all the differences are less than the pre-set threshold.