Historian Counter Retrieval for Rollover and Reset Classification
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Solution Overview
Problem
Industrial process control systems face challenges in accurately processing raw data from sensors into useful information, particularly in distinguishing between rollovers, resets, and reversals in counter data, which affects the reliability of product quantity calculations.
Innovation Solution
A historian device in a process control environment processes counter data points by setting a deadband value to differentiate between rollovers, resets, and reversals, and calculates the total product quantity passed through a process element based on selected data points within a specified timeframe, using quality rule modes to handle NULL values and ensure accurate reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the historian device processes all count value data points including NULL values, then the completeness of data processing is improved, but the accuracy of product quantity calculation deteriorates due to inability to distinguish between rollovers, resets, and reversals
Solution Approach 1:
The patent segments the data processing by introducing a deadband value that creates distinct ranges for different events: rollovers (when count decreases by more than deadband), resets (when count decreases by exactly deadband), and reversals (when count decreases by less than deadband). This segmentation allows accurate classification of each data point type while processing all data points including NULL values.
Solution Approach 2:
The patent changes the parameter of data processing by introducing a deadband threshold parameter that transforms the processing logic from simple value comparison to range-based classification. This parameter enables the system to distinguish between different types of count changes (rollovers, resets, reversals) by comparing the magnitude of change against the deadband value, thereby maintaining both completeness and accuracy.
2Measurement precision
If the historian device uses strict data quality filtering to ensure only valid data points are processed, then the accuracy of product quantity calculation is improved, but the productivity of data processing deteriorates due to exclusion of potentially useful data
Solution Approach 1:
The patent converts the previously harmful NULL values into beneficial data points by implementing an optimistic processing mode that treats NULL values as valid count values. This allows the system to process all available data points without exclusion, improving productivity while maintaining accuracy through the deadband-based classification mechanism that can handle various data conditions.
3Reliability
If the historian device implements complex quality rule modes to handle different data scenarios, then the reliability of data reporting is improved, but the device complexity increases
Solution Approach 1:
The patent implements dynamic processing modes (strict mode and optimistic mode) that can be selected based on the specific data scenario and requirements. The strict mode provides conservative processing with lower reliability but simpler logic, while the optimistic mode provides higher reliability through more sophisticated NULL value handling. This dynamic approach allows the system to adapt to different situations without requiring a permanently complex architecture.
Data Source
AI summary
Processing raw data stored in an historian device for determining an amount of products passed through a process element in a process control environment is described. A count value is incremented by a counter at a rate at which products pass through the process element. The count value rolls over to zero when the count value reaches a rollover value R. An historian device periodically receives count value data points from the counter. A deadband value D is set in the historian device for distinguishing between rollovers, resets, and reversals. A client device queries the historian device for an amount of products passed through the process element for a timeframe. The historian device selects a set of count value data points from within the queried timeframe. The historian device determines, based on the selected data points and their quality, an amount of products passed through the process element.