Adaptive Fill Level Measurement Using Dynamic Model Weighting
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Solution Overview
Problem
Classic averaging methods in level measurement technologies often lead to lag or inertia, causing delays in responding to changes in fill levels, which can result in overfilling or dry-running issues, especially when switching between filling and emptying a tank.
Innovation Solution
A fill level measuring device that uses a sensor device and a control unit to record measured values and update multiple independent mathematical models using Kalman filters, weighting and mixing them based on their predictive quality to accurately determine the fill level, thereby reducing noise and improving measurement accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If averaging methods are used to suppress noise in measured values, then measurement noise is reduced, but response time increases causing lag in detecting fill level changes
Solution Approach 1:
The patent implements dynamic adaptation of the averaging window size based on the detected situation (filling, emptying, or resting state). During filling or emptying operations, a smaller averaging window is used to maintain fast response, while during resting states, a larger window provides better noise suppression. This dynamic adjustment resolves the contradiction by making the noise suppression capability adaptive to operational requirements.
Solution Approach 2:
The system changes the parameter of averaging window size according to the operational state. By detecting whether the tank is filling, emptying, or at rest, the system automatically adjusts the number of measured values to average, thereby optimizing the balance between noise suppression and response time for each operational phase.
2Ease of manufacture
If a fixed averaging window is used, then implementation is simple, but accuracy decreases during transitions between filling and emptying
Solution Approach 1:
The system transitions from a static fixed averaging window to a dynamic adaptive window that changes based on operational state. By implementing situation detection (filling/emptying/resting) and automatically adjusting the averaging parameters, the system maintains high accuracy during transitions while preserving relatively simple implementation through automated state-based control.
3Adaptability or versatility
If manual parameterization of averaging is required, then flexibility is provided, but operational complexity increases
Solution Approach 1:
The system implements self-service by automatically detecting operational states (filling, emptying, resting) and autonomously adjusting averaging parameters without requiring manual intervention. This eliminates the need for users to manually parameterize averaging settings while maintaining the flexibility to adapt to different operational conditions, thereby resolving the contradiction between adaptability and ease of operation.
Data Source
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AI summary
Model-based noise reduction is used in level gauges, employing independent mathematical models whose accuracy is calculated by comparison with measured values. The models are then weighted and combined, or a single model is selected to calculate the level at a later time. This results in effective noise reduction.