Multi-Transducer Calibration Drift Detection for Measurement Accuracy
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Solution Overview
Problem
Current measurement systems face challenges in determining optimal recalibration intervals, leading to either premature calibration and increased costs or risking out-of-tolerance conditions due to varying calibration drift rates among devices.
Innovation Solution
A measurement system that uses multiple transducers to make independent measurements, deriving a combined value and detecting calibration drift by comparing individual measurements to an average or weighted average, allowing for extended recalibration intervals while ensuring measurement accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recalibration interval is shortened to ensure accurate measurements, then measurement accuracy is improved, but calibration frequency increases leading to increased direct and indirect calibration costs
Solution Approach 1:
The measurement device performs self-diagnosis by automatically monitoring its own calibration condition through internal sensors and processing units. The device generates calibration drift indicators and determines when recalibration is actually needed, eliminating the need for frequent external recalibrations and reducing calibration costs while maintaining measurement accuracy.
Solution Approach 2:
The system continuously monitors calibration drift in real-time and provides feedback about the actual calibration condition. Based on this feedback, the system dynamically determines recalibration timing, allowing extensions of recalibration intervals when drift remains within acceptable ranges, thus reducing unnecessary recalibrations while ensuring accuracy when needed.
2Loss of energy
If recalibration interval is extended to reduce calibration frequency, then calibration costs are reduced, but risk of out of tolerance conditions increases
Solution Approach 1:
The measurement device autonomously monitors its calibration condition using internal sensors and processing units. It generates calibration drift indicators that reflect the actual state of the measurement device, enabling reliable extension of recalibration intervals based on real condition assessment rather than conservative fixed schedules.
Solution Approach 2:
The system performs preliminary self-diagnosis and generates calibration drift indicators before actual recalibration is needed. This advance monitoring allows the system to predict when recalibration will be necessary, enabling planned extensions of recalibration intervals while maintaining reliability by acting before out-of-tolerance conditions occur.
3Ease of operation
If predictive statistical method is used to determine recalibration interval, then recalibration scheduling is simplified, but ability to identify outliers with deviating behavior is lost
Solution Approach 1:
Each measurement device performs autonomous self-diagnosis and generates its own calibration drift indicators based on its actual performance. This eliminates the need for statistical population-based scheduling and enables identification of individual device outliers, as each device reports its true condition rather than following a generalized schedule.
Solution Approach 2:
The system transitions from using fixed statistical parameters based on population data to using dynamic, device-specific calibration drift indicators. This parameter change allows the system to adapt to individual device behavior patterns, identifying outliers through actual performance data rather than statistical averages.
Data Source
AI summary
A measurement system uses a plurality of transducers that may differ from each other in at least one respect, such as having different operating principles or being made by different manufacturers. Respective measurement values obtained from the transducers are applied to a processor which provides a measured value based on the measurement values from a plurality of the transducers. The processor also provides information about the calibration drift of each of the transducers based upon a comparison between the measurement value obtained from the transducer to a value obtained from a combination of respective measurement values obtained from a plurality of the transducers. The calibration drift information provides an objective evaluation about the calibration condition of each of the transducers. When a transducer is determined to be outside of its calibration tolerance, a calibration needed alert occurs.