Field Device Compliance Prediction Using Filtered Deviation Trends
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Solution Overview
Problem
Current methods for predictive monitoring of field devices struggle to accurately determine the remaining time until a characteristic becomes non-compliant with specified requirements, especially when uncertainty and time dependency are unpredictable, and require additional information or fixed data rates.
Innovation Solution
A method involving continuous monitoring of deviations between measured and reference values, applying filters, determining noise, and using Monte Carlo simulations based on these deviations to estimate the remaining time until compliance is breached, accounting for average rate of change and uncertainty without requiring fixed data rates or additional knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulation is used to determine remaining time until non-compliance, then predictive accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary filtering and statistical analysis on deviation data before feeding it to the Monte Carlo simulation. By pre-processing the data to extract meaningful patterns and reduce noise, the simulation receives cleaner input data, which improves predictive accuracy while reducing the computational burden during the actual prediction process.
Solution Approach 2:
The patent introduces intermediate statistical parameters (such as mean deviation, standard deviation, and trend coefficients) that serve as mediators between the raw deviation data and the Monte Carlo simulation. These intermediaries simplify the input data structure while preserving the essential information needed for accurate prediction, thereby reducing computational complexity.
2Productivity
If service intervals are extended to reduce maintenance costs, then operational efficiency is improved, but device reliability may deteriorate
Solution Approach 1:
The patent transitions from static, fixed service intervals to dynamic, adaptive service intervals based on actual device condition. By continuously monitoring deviations and using Monte Carlo simulation to predict remaining time until non-compliance, the system adjusts service timing dynamically, extending intervals when the device is stable and reducing them when degradation is detected, thus maintaining both efficiency and reliability.
Solution Approach 2:
The patent implements a feedback mechanism where deviation data from ongoing operation is continuously collected, analyzed, and fed back into the prediction model. This feedback loop enables the system to learn from actual device behavior and adjust future service recommendations, ensuring that extended service intervals do not compromise reliability by detecting early signs of degradation.
3Reliability
If frequent monitoring is performed to ensure compliance, then device reliability is improved, but time and resource consumption increase
Solution Approach 1:
The patent enables the field device to perform self-diagnosis and self-monitoring by equipping it with sensors and processing capabilities to automatically detect deviations and generate compliance assessments. This self-service approach eliminates the need for frequent manual inspections while maintaining continuous monitoring, thereby ensuring reliability without significant time loss.
Solution Approach 2:
The patent implements periodic monitoring at strategically determined intervals rather than continuous monitoring. By using the Monte Carlo simulation to predict when the next compliance check is needed, the system performs monitoring only at critical moments, reducing overall monitoring time while maintaining assurance of compliance through predictive timing.
Data Source
AI summary
A method for monitoring a measurement variable and performing predictive monitoring of a compliancy of a characteristic of a field device to a requirement is described. The method includes a step of determining a deviation between a measured value of a monitored variable and a reference value of the monitored variable. The deviation is indicative of a degree of compliancy to the requirement. The method also includes a step of applying a filter to the deviations based on the deviations and the filtered deviations. The method further includes a step of determining a noise superimposed on the filtered deviations. At the end of a monitoring interval, a time remaining until the deviations will exceed the deviation range is determined using a Monte Carlo simulation.