Sensor Calibration via Nested Optimization
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Solution Overview
Problem
Existing sensor calibration methods are complex and require knowledge of the sensor's functioning, and adaptive methods often require extensive training data, making them inefficient for recalibration after installation, where environmental changes like soldering or stress can affect accuracy.
Innovation Solution
A method involving an inner optimization step to create a sensor-specific model based on measured data and an outer optimization step to adapt a general sensor model, using neural networks to minimize sensor errors and improve output accuracy, allowing for efficient recalibration without extensive user knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods are used, then sensor accuracy can be improved, but the complexity of the calibration process increases and requires specialized knowledge
Solution Approach 1:
The calibration method enables the sensor system to perform self-calibration without requiring external specialized knowledge or complex manual procedures. The processor automatically executes the two-stage optimization process, allowing the system to calibrate itself based on measured data and the general sensor model.
Solution Approach 2:
A general sensor model is created in advance through outer optimization using data from multiple sensors, which serves as a pre-trained framework. This preliminary model reduces the calibration burden during actual use, as individual sensors only need to undergo inner optimization rather than complete recalibration.
2Adaptability or versatility
If adaptive calibration methods are used, then sensor adaptation to new conditions is improved, but extensive training data is required
Solution Approach 1:
The calibration process is segmented into two distinct stages: outer optimization that creates a general sensor model using data from multiple sensors, and inner optimization that adapts individual sensors using their specific measured data. This segmentation allows efficient adaptation with reduced data requirements for individual sensor calibration.
Solution Approach 2:
The general sensor model serves as a universal framework that can be applied across multiple sensor types and conditions. This meta-model captures common characteristics of structurally identical sensors, enabling the system to adapt to new conditions without requiring extensive training data for each individual sensor.
3Reliability
If sensor recalibration is performed after installation, then accuracy degradation due to environmental changes is addressed, but the recalibration process becomes more difficult
Solution Approach 1:
The sensor system performs automatic self-recalibration after installation without requiring user intervention or specialized knowledge. The processor executes the inner optimization process using the stored general sensor model and measured data from the installed sensor, making recalibration as simple as collecting new measured data.
Solution Approach 2:
The general sensor model is prepared in advance during manufacturing, containing the optimized framework from outer optimization. This preliminary preparation enables easy post-installation recalibration, as the system only needs to perform inner optimization with new measured data rather than starting from scratch.
Data Source
AI summary
A method for calibrating a sensor of a sensor system. A plurality of sensors structurally identical to the sensor of the sensor system and a general sensor model are provided. An inner optimization step is subsequently carried out for each of the structurally identical sensors. During the inner optimization step, a sensor-specific sensor model is initialized using the general sensor model and a sensor-specific model parameter is subsequently optimized based on measured data of the sensor. The sensor-specific sensor model is adapted with the aid of the sensor-specific model parameter. An outer optimization step is then carried out. In this step, the sensor-specific sensor models adapted for each sensor are used in order to optimize the general sensor model. The general sensor model is stored in a memory of the sensor system.