Plant Sensor Verification Models for Drift-Aware Calibration
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Solution Overview
Problem
Existing methods for verifying sensor information in plant processes, such as water treatment, are inadequate in detecting drifting sensor readings that can lead to inefficient operation and non-compliance, as they rely on interval-based comparisons and calibrations, which are resource-intensive and may miss issues between scheduled checks.
Innovation Solution
A method using a verification model that compares received sensor information with expected information based on other plant process data, such as second sensor readings, process parameters, and control activities, to output a verification signal when deviations occur, allowing for event-based calibration and ensuring accurate sensor readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If interval-based comparison and calibration of sensor devices is performed, then sensor reading accuracy is maintained, but time consumption and resource usage increase
Solution Approach 1:
The patent implements a feedback mechanism where sensor readings are continuously verified against expected values derived from process models and other sensor data. This creates a real-time validation system that only triggers calibration when deviations indicate actual drift, eliminating the need for routine interval-based calibrations while maintaining measurement accuracy.
Solution Approach 2:
The system performs self-verification by using its own process data and models to validate sensor readings. The verification model automatically detects drifting sensor readings by comparing sensor information against expected values calculated from process parameters and relationships, enabling the system to identify calibration needs without external intervention or scheduled maintenance.
2Reliability
If interval-based calibration is performed, then sensor device reliability is maintained, but productivity decreases due to frequent maintenance
Solution Approach 1:
The continuous feedback loop constantly monitors sensor readings against expected values and automatically triggers calibration only when verification fails indicate actual sensor drift. This on-demand calibration approach maintains sensor reliability while minimizing operational disruptions, as calibration activities are performed only when necessary rather than following fixed schedules.
Solution Approach 2:
The system transitions from static, predetermined calibration intervals to dynamic, condition-based calibration timing. The calibration schedule adapts in real-time based on actual sensor performance and process conditions, allowing the system to maintain reliability while optimizing productivity by performing maintenance only when verification failures indicate genuine calibration needs.
3Measurement precision
If verification model continuously monitors sensor information, then detection precision of faulty readings improves, but computational load increases
Solution Approach 1:
The verification model applies partial verification by focusing computational resources only on validating specific sensor readings that have verification failures, rather than continuously processing all sensor data at full depth. The system performs lightweight initial checks and only triggers intensive verification when anomalies are detected, reducing overall computational energy consumption while maintaining high detection precision for faulty readings.
Data Source
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AI summary
An aspect of the invention relates to a method for verifying the plausibility of sensor information sensed by a sensor device associated with a plant process, in particular a water treatment process and/or a wastewater treatment process, wherein the sensor information relates to the plant process, the method comprising: receiving (S100), by a control unit, the sensor information; performing (S110), by the control unit, a verification model, the verification model determining an expected sensor information of the sensor device based on at least one other information related to the plant process and having a relationship to the sensor information; comparing the expected sensor information with the received sensor information; and upon determining (S120) that the sensor information deviates from the expected sensor information, outputting (S130), by the control unit, a verification signal.