Redundant Sensor Correlation Screening for Real-Time Data Quality
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Solution Overview
Problem
Existing methods for validating data from redundant weather sensors are inadequate in identifying faulty or noisy sensors in real-time, leading to potential errors in critical aircraft safety decisions and climatological observations.
Innovation Solution
A method and system that calculate time correlation coefficients, autocorrelation coefficients, and sensor correlation coefficients from multiple redundant sensors to determine a quality-controlled sensor set with the highest confidence level, dynamically filtering out faulty sensors and providing accurate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant sensors are used to improve measurement accuracy, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the sensor system into individual sensor units, each with its own quality control evaluation. By dividing the redundant sensor set into separatable components that can be independently assessed using correlation coefficients, the system manages complexity through modular evaluation rather than treating the entire system as a single unit.
Solution Approach 2:
The patent introduces correlation coefficients as new evaluation parameters to assess sensor data quality. By changing from simple data comparison to statistical correlation analysis (time correlation, autocorrelation, and sensor correlation coefficients), the system achieves more reliable identification of faulty sensors while maintaining manageable complexity through mathematical transformation.
2Measurement precision
If post-collection quality control is performed to identify faulty sensors, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs quality control evaluation continuously as data is collected, rather than waiting until after collection. By calculating correlation coefficients in real-time during the data collection process, the system identifies faulty sensors before they can significantly degrade the overall data quality, thus reducing the time loss associated with post-collection analysis.
Solution Approach 2:
The patent implements a feedback mechanism where correlation coefficient results are continuously monitored and used to adjust sensor selection. When a sensor's correlation coefficients indicate faulty performance, the system provides feedback to exclude that sensor from further analysis, creating a dynamic quality control process that adapts in real-time rather than requiring complete post-processing.
3Reliability
If traditional quality control methods are used to filter erroneous data, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal quality control methodology that can be applied to any redundant sensor system regardless of the specific sensor type or application. The correlation coefficient approach serves multiple functions: identifying faulty sensors, evaluating data quality, and guiding sensor selection, thereby reducing the need for application-specific complex quality control systems.
Solution Approach 2:
The patent replaces complex mechanical or manual quality control systems with mathematical computation. Instead of using physical filtering mechanisms or manual data review processes, the system uses correlation coefficient calculations to automatically identify and filter erroneous data, substituting mechanical complexity with computational efficiency.
4Reliability
If real-time sensor validation is implemented to improve aircraft safety decisions, then reliability is improved, but use of energy increases
Solution Approach 1:
The patent applies partial quality control by focusing computational resources on calculating only the necessary correlation coefficients (time correlation, autocorrelation, and sensor correlation) rather than performing exhaustive analysis of all sensor data. This partial action approach provides sufficient reliability for safety decisions while minimizing energy consumption compared to complete data validation.
Data Source
AI summary
A method for determining a quality controlled sensor set from a redundant sensor set comprising calculating a first time correlation coefficient and a first autocorrelation coefficient based on a first sensor time series data, calculating a second time correlation coefficient and a second autocorrelation coefficient based on a second sensor time series data, calculating a first and second sensor correlation coefficient based on the first sensor time series data and the second sensor time series data, and determining the quality controlled sensor set with a highest confidence level.