Condition-Based Power Plant Sensor Calibration
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Solution Overview
Problem
Traditional power plant sensor calibration schedules are fixed and do not account for the actual need of sensors, leading to unnecessary calibrations and potential undetected issues due to sensor drifting and equipment performance degradation.
Innovation Solution
A condition-based system that receives data from power plant sensors, reconciles errors, generates performance models, detects anomalies, and tunes these models to determine optimal calibration intervals, distinguishing between sensor issues and equipment degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed calibration schedules are used, then all sensors are calibrated regularly, but unnecessary calibrations are performed and calibration costs increase
Solution Approach 1:
The patent transitions from static fixed schedules to dynamic condition-based calibration scheduling. Sensors are monitored continuously and calibration is triggered only when actual degradation conditions are detected, making the calibration schedule adaptive and dynamic rather than predetermined and static.
Solution Approach 2:
The system enables sensors to essentially self-diagnose their calibration needs through continuous monitoring and analysis of sensor data patterns. The automated detection of drift conditions allows the system to identify which sensors require calibration without manual intervention, making the calibration process self-regulating.
2Reliability
If fixed calibration schedules are used, then calibration coverage is comprehensive, but sensors that do not need calibration are still calibrated
Solution Approach 1:
The system implements continuous feedback loops where sensor data is constantly monitored, analyzed for drift patterns, and used to trigger calibration only when needed. This feedback mechanism replaces blanket scheduled calibration with targeted calibration based on actual sensor performance conditions.
Solution Approach 2:
The patent monitors changes in sensor output parameters and calibration status over time. By tracking parameter drift and comparing against thresholds, the system determines when calibration parameters need adjustment, enabling precise calibration timing rather than fixed schedule adherence.
3Productivity
If calibration is delayed until the next schedule, then maintenance resources are optimized, but sensor drift and equipment degradation go undetected
Solution Approach 1:
The system performs preliminary detection and analysis of sensor drift conditions before actual calibration is needed. By continuously monitoring and identifying degradation trends early, the system prepares for calibration at the optimal moment, preventing undetected drift while optimizing maintenance timing.
4Measurement precision
If continuous sensor monitoring is implemented, then calibration needs are accurately identified, but system complexity increases
Solution Approach 1:
The patent introduces performance models as intermediary components that simplify the monitoring process. These models serve as mediators between raw sensor data and calibration decisions, analyzing data patterns and translating them into actionable calibration triggers, thereby reducing the complexity of direct sensor monitoring and analysis.
Data Source
AI summary
Embodiments of the invention can provide systems and methods for condition-based power plant sensor calibration. According to one embodiment of the invention, a system can be provided. The system can include a computer processor. The system can also include a memory operable to store computer-executable instructions operable to: receive data from the one or more power plant sensors; reconcile detected errors within the data from the one or more power plant sensors; calibrate the one or more power plant sensors based at least in part on the detected errors within the data; generate at least one performance model based at least in part on the reconciled data from the one or more power plant sensors; detect anomalies within the at least one performance model; and tune the at least one performance model to account for the anomalies.