Sensor Status Monitoring Using Bayesian Control Lines
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Solution Overview
Problem
Performance degradation or failure of sensors due to lifetime limits or external shocks leads to reduced accuracy and reliability of measured data, necessitating real-time monitoring and analysis to validate sensor validity.
Innovation Solution
A device and method using a processor with memory to determine the operational status of a sensor by establishing a correlation between a target sensor and a reference sensor through Bayesian models and polynomial regression, setting control lines based on credible intervals, and adjusting these lines dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and analysis of sensor data is implemented to validate sensor validity, then sensor operational status can be determined, but system complexity increases
Solution Approach 1:
The patent introduces control lines as intermediary elements that mediate between sensor data and validity determination. These control lines serve as threshold boundaries that automatically indicate whether sensor data falls within acceptable ranges, simplifying the complexity of real-time validation by providing clear decision boundaries without requiring complex analysis algorithms.
Solution Approach 2:
The patent dynamically adjusts control line parameters based on historical sensor data and operational conditions. By changing the parameters of control lines (threshold values, ranges) according to observed sensor behavior patterns, the system adapts to varying conditions while maintaining reliable validity determination, avoiding the need for fixed complex validation rules.
2Measurement precision
If control lines are set based on historical data correlation, then sensor validity can be intuitively monitored, but computational resources increase
Solution Approach 1:
The patent performs preliminary analysis by establishing control lines based on historical sensor data correlations before real-time monitoring begins. This pre-processing step creates ready-to-use threshold boundaries that can be applied during operational monitoring without requiring intensive real-time computation, thus reducing computational resource usage during actual sensor validation while maintaining precise monitoring capability.
Data Source
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AI summary
A device for determining the operational status of a sensor is configured to: determine initial parameters of a Bayesian model and a degree of a polynomial regression model based on historical data of a target sensor and a reference sensor; infer posterior distributions of regression coefficients and an error term of a regression curve using the polynomial regression model and the Bayesian model; set a credible interval based on the posterior distributions of the regression coefficients and the error term of the regression curve, and set control lines of data of the target sensor using the credible interval; determine an accuracy of the target sensor based on current data of the target sensor and the set control line; and control an operational status of the target sensor based on the accuracy.