Field Device Measurement Algorithms Adapted to Environmental Drift
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Solution Overview
Problem
Automation field devices face challenges in maintaining measurement accuracy due to varying stress parameters such as temperature, pressure, and chemical conditions, requiring frequent and time-consuming recalibrations to adapt to environmental influences.
Innovation Solution
A method where field devices capture and store calibration data and environmental information, creating a mathematical model to adapt measuring algorithms based on these data, which are then shared among similar devices to improve measurement performance, potentially eliminating the need for recalibrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If repeated calibrations are performed to maintain measurement accuracy under varying environmental conditions, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary calibration in the laboratory under controlled conditions before deployment. The field device then uses pre-determined calibration data combined with real-time environmental monitoring to maintain measurement accuracy without requiring repeated field calibrations, thus saving time while preserving precision.
Solution Approach 2:
The patent replaces the mechanical/manual calibration process with an automated system that uses environmental sensors to monitor conditions and a processing unit to adjust measurements based on pre-stored calibration data. This substitution eliminates the need for repeated manual calibration operations while maintaining measurement accuracy.
2Adaptability or versatility
If field devices are equipped with environmental sensors and data processing capabilities to adapt to environmental influences, then adaptability is improved, but device complexity increases
Solution Approach 1:
The field device integrates multiple functions into a single unit: the microcontroller serves as both the processing unit for measurements and the control unit for environmental monitoring. The same processor analyzes environmental sensor data and adjusts measurements accordingly, eliminating the need for separate dedicated components and reducing overall system complexity.
Solution Approach 2:
The patent combines the calibration data storage, environmental sensor data acquisition, and measurement processing functions into a single integrated system. The processing unit receives both measurement signals and environmental data, correlates them using pre-stored calibration information, and outputs corrected measurements, thereby simplifying the system architecture while enhancing adaptability.
3Measurement precision
If calibration data and environmental information are captured and stored for mathematical modeling, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system captures and stores calibration data and environmental information at defined, periodic time intervals rather than continuously. This periodic sampling approach maintains measurement precision by capturing sufficient data for accurate mathematical modeling while significantly reducing energy consumption compared to continuous data acquisition.
Solution Approach 2:
The system uses low-power microcontrollers and energy-efficient sensors that consume minimal energy during operation. The processing unit performs simple correlation calculations using pre-stored calibration data rather than complex real-time computations, thereby maintaining measurement accuracy while minimizing energy usage.
Data Source
AI summary
Disclosed is a method for improving the measuring performance of automation field devices, wherein each of the field devices determines a process variable using a measuring algorithm and is exposed to measurable environmental influences. The method includes capturing the calibration data of the field devices and capturing an item of environmental information of the field devices at defined time intervals; storing the environmental information, the calibration data, and a time stamp in a database; selecting a group of field devices which determine a process variable using the same measuring algorithm and which are exposed to the same environmental influences; correlating the environmental information and calibration data captured over time; creating a mathematical model relating the calibration data and the environmental information; adapting the measuring algorithm on the basis of the model; and transmitting the adapted measuring algorithm to all field devices in the group.
