Sensor Status Validation Using Bayesian Control Lines
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Solution Overview
Problem
Existing sensor technologies face performance degradation and failure due to lifetime limits and external shocks, leading to reduced accuracy and reliability, necessitating real-time monitoring and validation to ensure continuous facility operation.
Innovation Solution
A device and method using a processor with memory to determine the operational status of a sensor by correlating historical data from a target sensor with a reference sensor through Bayesian and polynomial regression models, setting control lines based on credible intervals, and validating the sensor's accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and analysis of sensor data is implemented to validate sensor validity, then reliability of sensor data is improved, but device 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 reference boundaries derived from historical data and domain knowledge, allowing the system to validate sensor readings by comparing them against predefined thresholds rather than requiring complex real-time analysis algorithms
Solution Approach 2:
The system performs preliminary actions by establishing control lines based on historical sensor data and expert knowledge before actual validation occurs. These control lines are pre-computed confidence intervals that encapsulate expected sensor behavior under normal conditions, enabling rapid validity assessment without complex real-time computations
2Measurement precision
If control lines are set based on credible intervals from Bayesian and polynomial regression models to determine sensor accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent transforms the complex Bayesian and polynomial regression models into simplified control line parameters that can be directly applied for validation. By converting model outputs into confidence interval boundaries, the system maintains measurement precision while reducing computational complexity during actual sensor validation operations
Solution Approach 2:
The system creates simplified copies of the complex model outputs in the form of control lines. These control lines replicate the essential validation information from the Bayesian and polynomial regression models without requiring the full computational machinery, enabling efficient accuracy determination
Data Source
AI summary
Disclosed is a device for determining the operational status of a sensor 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 a posterior distribution of a regression coefficient 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 distribution of the regression coefficient 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.