Predictive Monitoring of Measurement Accuracy During Operation Phases
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Solution Overview
Problem
Current methods for monitoring the measurement accuracy of devices in facilities require frequent calibrations, which are time-consuming, costly, and often disrupt operations, especially in facilities with stringent hygiene or sterility standards, and cannot reliably predict when measurement errors will exceed permissible limits.
Innovation Solution
A predictive monitoring method that continuously records data during facility operation to identify specific operation phases with distinct characteristics, using classification methods to determine compliance indicators and calculate the remaining time before measurement accuracy drops below a minimum threshold, allowing for calibration optimization without reference measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent calibrations are performed to ensure measurement accuracy, then measurement precision is improved, but productivity deteriorates due to operational interruptions and time loss
Solution Approach 1:
The system performs preliminary actions by continuously monitoring measurement device characteristics and predicting future accuracy degradation during normal operation. This allows calibration to be scheduled in advance based on actual device performance trends rather than following a fixed preventive maintenance schedule, enabling calibration to occur just before accuracy degradation becomes problematic, thus minimizing operational interruptions.
Solution Approach 2:
The system implements feedback by continuously measuring characteristics of the measurement device during operation, comparing these measurements against reference values, and using the deviations to predict future accuracy issues. This feedback loop enables dynamic adjustment of calibration timing based on actual device behavior, allowing operations to continue longer between calibrations when the device performs well, while ensuring calibration occurs promptly when degradation is predicted.
2Reliability
If frequent calibrations are performed to maintain measurement accuracy, then reliability is improved, but loss of time increases due to calibration interruptions
Solution Approach 1:
The system performs preliminary monitoring and prediction actions during normal operation to assess measurement device accuracy trends before actual degradation occurs. By continuously tracking device characteristics and predicting future accuracy levels, the system prepares in advance for calibration only when necessary, avoiding unnecessary calibration interruptions while ensuring reliability is maintained through timely calibration when predicted accuracy degradation is detected.
Solution Approach 2:
The system uses feedback from continuous monitoring of measurement device characteristics during operation to dynamically determine calibration timing. The feedback mechanism compares actual device performance against reference values and predicts future accuracy, enabling the system to maintain high reliability by calibrating when needed while minimizing time loss by avoiding calibration during periods when the device is performing within acceptable parameters.
3Productivity
If measurement devices are calibrated during operation to minimize interruptions, then productivity is improved, but measurement precision deteriorates due to inability to perform proper calibration procedures
Solution Approach 1:
The system introduces an intermediary prediction model that acts as a mediator between continuous operation and calibration requirements. Instead of performing direct calibration during operation (which would compromise accuracy), the system uses the prediction model to monitor device characteristics and forecast accuracy degradation, then schedules calibration at appropriate times when the device can be properly calibrated without operational constraints, thus maintaining both productivity and measurement precision.
4Measurement precision
If calibrations are performed to ensure measurement accuracy, then measurement precision is improved, but loss of substance increases due to consumption of calibration materials and resources
Solution Approach 1:
The system performs preliminary monitoring and prediction during normal operation to assess actual measurement device accuracy trends. By predicting when accuracy degradation will occur based on continuous characteristic measurements, the system enables calibration to be performed only when necessary, avoiding unnecessary calibration events that would consume calibration materials and resources, thus reducing substance loss while maintaining measurement precision.
Solution Approach 2:
The system changes the parameter being monitored from direct measurement accuracy (which requires material-intensive calibration) to measurement device characteristics that can be monitored during operation without consuming calibration materials. By tracking characteristics such as response time, signal amplitude, or other operational parameters that correlate with accuracy degradation, the system reduces calibration material consumption while still enabling timely calibration to maintain measurement precision.
Data Source
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AI summary
A method of predictive monitoring of a variable of a medium (5) located in a container (1) of a facility and of a measurement accuracy of a measurement device (3) measuring this variable is described, wherein the facility is operated independently of the measured values (m(ti)) and wherein operation of the facility includes a repeatedly occurring specific operation phase (Ps), wherein measured values (m(ti)) measured during the specific operation phases (Ps) exhibit a characteristic (C) distinguishing them from measured values measured (m(ti)) during other time periods, and wherein the characteristic (C) is compliant to a reference characteristic (Cr) when the facility was operating properly and the measurement device (3) was compliant to a specified measurement accuracy. The method comprises the steps of: during operation of the facility continuously recording data (D) comprising the measured values (m(ti)), based on training data included in this data (D) determining a classification method capable of identifying data sets (S) comprised in the recorded data (D), that have each been measured during one of the specific operation phases (Ps); performing the classification method and based on the data sets (S) identified by the classification method determining a time series (ts) of compliancy indicators (I) indicative of a degree of compliancy of at least one property of the characteristic (C) of the measured values (m(ti)) to a corresponding reference property of the reference characteristic (Cr); based on the time series (ts) at least once determining a remaining time (RT) remaining until the degree of compliancy indicated by compliancy indicators (I) will drop below a predetermined minimum degree of compliancy (Imin); and providing an output informing about the remaining time (RT).