Digital Twin Sensor Validation via Virtual-Physical Deviation
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Solution Overview
Problem
Current systems lack effective tools for automatically validating physical and virtual sensors in oil or gas facilities, leading to potential faults and shutdowns due to incorrect sensor readings, and they cannot determine whether actual or virtual sensors require revalidation and recalibration.
Innovation Solution
A computer-implemented method that monitors readings from actual and virtual sensors, calculates deviations, and uses proximity sensor readings to determine if either or both sensors need revalidation and recalibration, integrating with digital twins for continuous online validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple physical sensors are used for the same parameter, then measurement reliability is improved, but system cost and complexity increase
Solution Approach 1:
The patent creates a virtual sensor that copies the function of physical sensors by using a digital twin model to simulate sensor readings. This virtual sensor provides redundant measurement capability without requiring additional physical hardware, thereby improving reliability while avoiding the complexity and cost of installing multiple physical sensors.
Solution Approach 2:
The digital twin acts as an intermediary between the physical process and the measurement system. It receives process data from the physical system, processes it through a validated model, and produces sensor readings that can be used for control decisions, eliminating the need for direct multiple physical sensor installations.
2Device complexity
If conventional sensor validation tools are not used, then system simplicity is maintained, but the ability to detect invalid sensor readings is lost
Solution Approach 1:
The system continuously compares virtual sensor readings against physical sensor readings and uses this feedback to detect when a physical sensor becomes invalid. The digital twin model provides expected readings based on the actual process state, creating a feedback loop that automatically identifies sensor failures without requiring complex validation infrastructure.
Solution Approach 2:
The digital twin model serves itself by automatically validating sensor readings through continuous comparison with the physical system state. The validation process is self-contained within the digital twin, requiring no external validation tools or systems, thus maintaining simplicity while ensuring reliability.
3Device complexity
If no validation mechanism is implemented, then system complexity is minimized, but faults and shutdowns occur due to incorrect readings
Solution Approach 1:
The digital twin model performs preliminary validation of sensor readings by continuously simulating expected process states and comparing them against actual sensor data. This preliminary detection of invalid readings prevents faults from propagating to the control system, avoiding shutdowns before they occur without adding significant complexity.
Data Source
AI summary
Systems and methods include a method for monitoring sensors. Readings of actual field sensors corresponding to virtual sensors of a virtual model are monitored. A deviation between an actual sensor reading (ASR) and a virtual sensor reading (VSR) is determined. When only the VSR is deviating and when sensor readings of sensors proximate to the actual sensor correspond to the VSR, then the ASR is invalid and the actual sensor requires revalidation/recalibration. When only the ASR is deviating and when sensor readings of sensors proximate to the actual sensor correspond to the ASR, then the VSR is invalid and the virtual sensor requires revalidation/recalibration. When both the VSR and ASR are deviating from design ranges and when sensor readings of sensors that are proximate to the actual sensor do not correspond to either the ASR or the VSR, then both the ASR and VSR are invalid, requiring revalidation/recalibration.