Multihead Scale Signal Processing for Faster Weight Estimation
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Solution Overview
Problem
Existing signal processing methods for multihead scales suffer from long settling times and inaccuracies due to disturbance variables, leading to delayed measurement results and reduced throughput in combination scales.
Innovation Solution
A signal processing method using artificial neural networks to rapidly estimate the second constant weight value by preprocessing measurement signals, incorporating features like maxima, minima, and gradients, and adjusting stability criteria based on prediction accuracy, combined with digital filters to correct and stabilize the measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional filtering methods are used to eliminate disturbance variables, then measurement accuracy is improved, but measurement time increases significantly
Solution Approach 1:
The patent applies dynamic filtering where the filter characteristics change over time based on the measurement state. During the transient phase after product discharge, a first filter with different characteristics is applied, and during the stable phase, a second filter with different characteristics is applied. This dynamic adaptation allows faster response during transient conditions while maintaining accuracy during stable conditions, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The system performs preliminary classification of the measurement signal state to determine whether to apply the first or second filter. By pre-identifying the measurement phase (transient vs. stable) and selecting the appropriate filter beforehand, the system avoids the time penalty of adaptive adjustments and directly applies the optimal filter for the current condition, reducing overall measurement time while maintaining accuracy.
2Measurement precision
If the weighing system waits for complete signal stabilization, then measurement accuracy is improved, but throughput decreases
Solution Approach 1:
The patent applies partial filtering by using the first filter only during the critical transient phase when disturbance variables are present, rather than applying strong filtering throughout the entire measurement process. This partial application of filtering action removes the harmful transient effects while avoiding the excessive smoothing that would delay the measurement result, thereby maintaining both accuracy and throughput.
Solution Approach 2:
The measurement process is segmented into distinct phases (transient phase and stable phase), with different filtering strategies applied to each segment. The first filter handles the transient phase with its specific characteristics, and the second filter handles the stable phase with different characteristics. This segmentation allows the system to optimize for speed during the transient phase and accuracy during the stable phase without compromising either, resolving the throughput-accuracy contradiction.
Data Source
AI summary
The present invention relates to a signal processing method for weight signals (W) of scales, in particular combination scales (K).Signal processing is performed using preprocessed discrete values (W(i)) of the weight signal (W), which are supplied to at least one artificial neural network. With the help of this at least one artificial neural network, an estimated value (SW) for the actual weight is determined, for example in a weighing device of a combination scale. This is performed faster than if waiting for the actual weight signal. The estimated values (SW) are forwarded to the combination scale (KW), which uses them to form combinations.