Calibration Method Detecting Impaired Measurement Properties
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Solution Overview
Problem
Current calibration methods for measurement devices are inadequate in detecting impaired measurement properties and systematic errors, as they rely solely on maximal permissible errors, which can lead to undetected issues even if measurement indications have a low probability of occurrence due to device impairments, and fail to distinguish between device and calibration process-related errors.
Innovation Solution
A method that involves determining predefined characteristic properties of measurement indications and comparing them to threshold ranges based on statistical distributions from similar devices, with refinements that include probability density functions and repeated measurements to assess reliability and compliance, allowing for early detection of impaired measurement properties and setting optimal recalibration intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If calibration is performed based solely on maximal permissible error (MPE), then conformity declaration is simplified and faster, but impaired measurement properties and systematic errors remain undetected
Solution Approach 1:
The calibration assessment is segmented into multiple independent evaluation criteria: MPE compliance check, statistical distribution analysis, and characteristic property evaluation. Each segment addresses different aspects of measurement quality, allowing comprehensive detection while maintaining efficient processing through modular assessment steps
Solution Approach 2:
The method performs additional statistical analyses beyond the minimum MPE check. By calculating probability distributions and evaluating multiple characteristic properties (linearity, hysteresis, repeatability), the system exceeds the basic calibration requirement to achieve more reliable detection of impaired measurement properties
2Reliability
If statistical analysis of measurement indications is performed to detect low probability events, then detection of impaired devices is improved, but calibration complexity and computational requirements increase
Solution Approach 1:
The patent replaces complex manual calibration assessment with automated statistical computing. Probability distributions and characteristic property calculations are performed through computational algorithms rather than manual analysis, reducing procedural complexity while enhancing detection reliability
Solution Approach 2:
The method transforms calibration assessment from binary pass/fail based on MPE to a multi-parameter statistical evaluation. By analyzing probability distributions, mean values, standard deviations, and multiple characteristic properties simultaneously, the system achieves comprehensive detection while managing complexity through standardized computational procedures
3Measurement precision
If multiple characteristic properties are evaluated beyond MPE, then measurement quality assessment is improved, but calibration time and processing requirements increase
Solution Approach 1:
The calibration method processes multiple characteristic properties continuously from the same measurement data set. Instead of performing separate measurement campaigns for each property, the system calculates MPE compliance, statistical distributions, and characteristic properties (linearity, hysteresis, repeatability) from a single continuous data acquisition process, eliminating redundant measurement time
Data Source
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AI summary
A method of calibrating a measurement device capable of providing more detailed information on measurement properties of the device, is described, wherein the measurement device performs measurements according to at least one predefined operating procedure, during which at least one given value (QR) of a quantity to be measured by the device is provided by a corresponding reference or standard, and measured and indicated by the device, the indicated measurement indications (Ml) and the corresponding given values (QR) of the measured quantity are recorded, at least one predefined characteristic property (E) of at least one of the measurement indications (MI) is determined and compared to corresponding threshold range (ER), wherein each threshold range (ER) was previously determined based on a statistically representative distribution of the values of the respective property (E) determined based on measurement indications (Ml) derived during execution of a statistically representative number of performances of measurements according to the respective operation procedure with measurement devices of the same type as the device under calibration, and a potentially impaired measurement property of the device under calibration is indicated if at least one determined characteristic property (E) exceeds the respective threshold (ER).