Historian Counter Retrieval for Rollover-Aware Product Counts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In process control environments, existing systems face challenges in accurately processing raw data from counters into useful information, particularly in distinguishing between rollovers, resets, and reversals, which affects the accuracy of product quantity calculations.
Innovation Solution
The implementation of a historian device that receives count value data points from counters, sets a deadband value to differentiate between these events, and calculates the total product quantity passed through a process element by incrementing or decrementing the count value based on detected rollovers, resets, and reversals, while also handling NULL data points and quality rule modes to ensure accurate data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the historian device uses traditional data retrieval methods without quality rule modes, then the system complexity is low, but the measurement precision of product quantity calculations deteriorates due to inability to distinguish between rollovers, resets, and reversals
Solution Approach 1:
The patent segments the data quality assessment into distinct quality rule modes (GOOD, EXTENDED, OPTIMISTIC) that can be selectively applied. Each mode represents a different level of data point filtering and processing rigor, allowing the system to handle rollovers, resets, and reversals with appropriate precision without uniformly increasing complexity across all operations.
Solution Approach 2:
The patent changes the parameter of data quality tolerance by introducing multiple quality rule modes. The GOOD mode applies strict quality filters, EXTENDED mode relaxes some filters, and OPTIMISTIC mode applies minimal filters. This parameter change allows the same historian device to adapt its processing precision based on operational needs, resolving the contradiction between precision and complexity.
2Measurement precision
If the historian device applies strict quality rules to filter all data points, then the measurement precision improves, but the productivity of data retrieval deteriorates due to increased processing time
Solution Approach 1:
The patent makes the data retrieval process dynamic by allowing the quality rule mode to be adjusted based on operational requirements. The system can switch between GOOD, EXTENDED, and OPTIMISTIC modes dynamically, enabling fast retrieval when precision is less critical and slower but more accurate retrieval when precision is paramount, thus resolving the static contradiction between speed and accuracy.
Solution Approach 2:
The patent applies partial quality filtering based on the selected mode. In OPTIMISTIC mode, minimal filtering is applied (partial action) to maximize speed, while in GOOD mode, comprehensive filtering is applied to maximize precision. This partial application of quality rules allows the system to balance productivity and measurement precision by applying only the necessary level of scrutiny for each query.
3Measurement precision
If the counter rollover value is increased to reduce rollover frequency, then the measurement precision of product quantity improves, but the device complexity increases due to larger data ranges and potential overflow issues
Solution Approach 1:
The patent implements feedback mechanisms that track counter behavior, rollover events, and data quality metrics. The system uses this feedback to automatically adjust quality rule modes and detect anomalies in counter operations. This feedback loop manages the complexity of high-value counters by providing automated monitoring and adjustment, resolving the contradiction between measurement precision and device complexity.
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.