Flow Control Valve State Detection With Prediction Error Feedback
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Solution Overview
Problem
Existing state detection methods, particularly for valves in temperature control systems, struggle to accurately predict abnormal states due to deviations in prediction values, leading to potential misestimation of abnormalities.
Innovation Solution
A state detection device and method that utilizes a numerical value acquisition unit, prediction value specification unit, and learned models to accurately specify prediction values by replacing them with actual values when deviations exceed a threshold, and adjusting settings based on error evaluation to notify abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a prediction model uses actual measured values as input to predict future values for abnormality detection, then the method can detect signs of abnormalities before they occur, but the prediction accuracy deteriorates when abnormalities are present in the input data, leading to inaccurate abnormality estimation
Solution Approach 1:
The system continuously compares actual measured values with predicted values and uses this feedback to detect abnormalities. When the deviation between actual and predicted values exceeds a threshold, an abnormality is detected, and the prediction model is retrained with corrected data to improve future prediction accuracy.
Solution Approach 2:
The prediction model predicts future values before abnormalities occur by analyzing current and historical data. This preliminary prediction allows the system to detect potential abnormalities in advance, enabling preventive maintenance before actual failures occur.
2Adaptability or versatility
If the prediction model continuously updates using actual measured values, then the model adapts to changing conditions, but false abnormality detections increase when prediction values deviate significantly from actual values
Solution Approach 1:
The system dynamically adjusts the threshold for abnormality detection based on the deviation between predicted and actual values. When deviations are within acceptable ranges, the system continues normal operation. When deviations exceed the dynamic threshold, the system triggers abnormality detection and initiates model retraining, balancing adaptability with reliability.
3Ease of operation
If a simple threshold comparison method is used to detect valve abnormalities, then the detection method remains simple and easy to implement, but the method cannot detect early signs of abnormalities before they manifest as apparent failures
Solution Approach 1:
The prediction model acts as an intermediary between simple threshold comparison and complex abnormality analysis. It translates normal operational data into predicted future values, which are then compared with actual values using a simplified threshold method. This intermediary layer enables early abnormality detection while maintaining implementation simplicity.
Data Source
AI summary
A state detection device including a numerical value acquisition unit that acquires an actual opening degree and a target opening degree of a flow rate control valve as specific numerical values continuously detected and/or specified, and a prediction value specification unit that specifies, based on the plurality of specific numerical values (actual opening degree and target opening degree) acquired by the numerical value acquisition unit, a prediction value of the actual opening degree that is a specific numerical value next to the plurality of specific numerical values. Then, when a difference between the prediction value corresponding to the actual opening degree and the actual opening degree corresponding to the prediction value is a predetermined value or more, the prediction value specification unit replaces the prediction value with the actual opening degree corresponding to the prediction value, and specifies the next prediction value and the subsequent prediction value.