Sensor Background Signal Analysis for Calibration Status
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Solution Overview
Problem
Existing methods for determining when sensors require calibration are overly conservative and time-consuming, especially in environments with many sensors, leading to frequent and costly calibrations.
Innovation Solution
A system that analyzes background signals from sensors to identify changes or drifts indicative of a need for calibration, using offset, noise, and frequency variations to determine if a sensor needs recalibration, thereby reducing unnecessary calibration events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are calibrated frequently based on conservative time periods, then sensor reliability is maintained, but time and cost are wasted on unnecessary calibrations
Solution Approach 1:
The system continuously monitors sensor background signals and provides feedback about sensor health status. By analyzing drift patterns and comparing them against thresholds, the system dynamically determines when calibration is actually needed, replacing fixed conservative schedules with adaptive feedback-driven calibration timing.
Solution Approach 2:
The sensor system performs self-diagnosis by monitoring its own background signals and drift characteristics. The sensor essentially monitors itself and triggers calibration requests only when self-diagnosed issues indicate actual calibration needs, eliminating the need for external systems to enforce conservative calibration schedules.
2Measurement precision
If active systems are used to monitor sensor condition, then calibration needs can be accurately identified, but system complexity increases
Solution Approach 1:
The invention extracts and analyzes only the background signal component from sensor output, separating it from the actual measurement signal. By focusing exclusively on monitoring background drift rather than analyzing complete sensor responses to various stimuli, the system achieves accurate calibration status detection with minimal additional complexity.
Solution Approach 2:
The system creates a simplified model or copy of sensor behavior by tracking background signal drift patterns over time. This copied behavioral model allows the system to predict calibration needs without requiring complex active testing systems, using instead a lightweight computational approach that mirrors sensor degradation patterns.
Data Source
AI summary
A system has at least one sensor and a control for analyzing a signal from the sensor. The sensor is operable to send a signal indicative of a presence of a particular occurrence to the control. The sensor also sends a background signal even without the presence of the particular occurrence. The control evaluates the background signal to identify a need for calibration. A method is also disclosed.