Sensor Neural Network Compensation for Measurement Inaccuracies
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Solution Overview
Problem
Sensors face measurement inaccuracies due to manufacturing variations and non-linear effects, which are difficult to compensate for, especially in environments where physical relationships are not fully understood, and there are limited methods to determine these variations, leading to suboptimal performance.
Innovation Solution
The use of an artificial neural network (ANN) to detect and compensate for external influences on sensors, such as temperature, by linking sensor output signals with surrounding information, allowing the network to learn and improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used to correct sensor sensitivity, then manufacturing variations can be partially compensated, but measurement precision remains insufficient due to unknown physical relationships and inability to determine variations
Solution Approach 1:
The patent introduces an artificial neural network as an intermediary system between the sensor and the measurement output. The ANN processes sensor signals and compensates for measurement errors caused by manufacturing variations and non-linear effects, achieving high measurement precision without requiring detailed knowledge of the underlying physical relationships.
Solution Approach 2:
The patent transforms the sensor measurement problem by changing from traditional calibration parameters to neural network weights and biases. Through training with measurement data, the ANN learns optimal parameter transformations that compensate for sensor inaccuracies, enabling adaptive correction of manufacturing variations and non-linear effects.
2Adaptability or versatility
If the sensor operates in dynamic environments with varying external influences, then adaptability is improved, but measurement precision deteriorates due to temperature and other environmental effects
Solution Approach 1:
The patent implements feedback mechanisms where the neural network continuously processes sensor inputs and environmental conditions, adjusting its compensation based on learned patterns. The system uses historical measurement data and real-time environmental information to dynamically correct sensor readings, maintaining precision across varying conditions.
Solution Approach 2:
The patent makes the compensation system dynamic by using a trained neural network that can adapt to changing environmental conditions. Unlike static calibration methods, the ANN can process varying temperature, pressure, and other environmental parameters, dynamically adjusting compensation to maintain measurement precision across different operating conditions.
Data Source
AI summary
A method is for operating a sensor. Influences on the sensor are detected and compensated for by an artificial neural network (ANN). The influences on the sensor are described by information from surroundings of the sensor, and an output signal of the sensor is linked in the ANN to further data representing the information from the surroundings of the sensor, such that a compensated output signal is obtained.