AI Sensor Recalibration Using Standard Measurement Comparison
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Solution Overview
Problem
Manual recalibration of numerous sensors in technical installations is labor-intensive, time-consuming, and costly due to the need for expert-level comparison of sensor readings with standard measurements, often resulting in unnecessary recalibration when errors are within acceptable margins.
Innovation Solution
A method and system utilizing an artificial intelligence model trained on calibration data to automatically determine calibration errors and generate feedback signals for recalibration, reducing the need for human intervention by analyzing sensor and standard measurement device readings using image processing and machine learning algorithms like KNN, and providing automated notification and instruction for recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recalibration of sensors is performed by comparing readings with standard measurement devices, then measurement precision is maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The sensor system performs self-calibration by automatically comparing its readings with standard measurement devices and adjusting its own calibration parameters without requiring manual intervention from engineers, thereby reducing recalibration time while maintaining precision
Solution Approach 2:
The patent replaces the manual mechanical process of comparing sensor readings with standard devices with an automated electronic system that uses processors to analyze data and generate calibration signals, eliminating the need for manual engineering intervention
2Measurement precision
If manual recalibration is performed with high expertise requirements, then calibration accuracy is ensured, but labor costs and operational complexity increase
Solution Approach 1:
The system automatically determines calibration errors and generates correction signals without requiring engineers to manually analyze readings or understand calibration criteria, making the operation simple while maintaining accuracy through automated expert-level processing
Solution Approach 2:
The patent introduces an automated calibration management system as an intermediary between sensors and standard measurement devices, which handles the complex task of comparing readings, determining errors, and generating calibration signals, thereby simplifying the operator's role
3Reliability
If sensors are recalibrated frequently to ensure accuracy, then measurement reliability is improved, but resource wastage increases
Solution Approach 1:
The patent implements dynamic recalibration scheduling that adjusts calibration frequency based on actual sensor performance degradation rates and process criticality, rather than using fixed intervals, thereby optimizing the balance between reliability and resource consumption
Solution Approach 2:
The system continuously monitors sensor readings and compares them with standard measurement devices to provide feedback on calibration status, enabling recalibration only when actually needed rather than on a fixed schedule, thus reducing unnecessary resource consumption
4Measurement precision
If manual comparison of sensor readings with standard devices is performed, then calibration error detection accuracy is maintained, but device complexity increases
Solution Approach 1:
The patent creates a universal calibration management system that can handle multiple sensor types, processes, and standard measurement devices through a single integrated platform, reducing overall system complexity by consolidating functions rather than requiring separate manual procedures for each sensor
Data Source
AI summary
A method and a system of recalibrating a plurality of sensors in a technical installation is provided. The method includes determining, by the processing unit, a first reading associated with a sensor of a plurality of sensor and a second reading associated with a standard measurement device. The method further includes determining, by the processing unit, whether the sensor is in an uncalibrated state by application of an artificial intelligence model on the first reading and the second reading. The method further includes outputting, by the processing unit, a notification to a user based on a determination that the sensor is in the uncalibrated state.