Context-Adaptive Historian Resolution for Industrial Event Data
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Solution Overview
Problem
Existing systems for recording and storing industrial control system data face challenges in efficiently managing bandwidth and storage while ensuring data integrity and resolution, particularly in handling high-frequency data components and event analysis.
Innovation Solution
A context-adaptive resolution system is implemented, where industrial control systems publish time-series data at varying resolutions based on event triggers. During normal conditions, data is published at a lower resolution to conserve resources, and upon event activation, data for relevant variables is published at a higher resolution to ensure detailed analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is published at high resolution continuously, then data integrity and analysis capability are improved, but bandwidth and storage resources are consumed excessively
Solution Approach 1:
The system dynamically adjusts data publication resolution based on operational context. During normal operations, data is published at lower resolution to conserve resources. When events are detected or requested, the system transitions to high-resolution data publication for relevant variables, thereby resolving the contradiction between maintaining data integrity and reducing resource consumption.
Solution Approach 2:
The system changes the resolution parameter of data publication based on event status. By monitoring operational parameters and event triggers, the system adjusts the data sampling rate and resolution dynamically, publishing high-resolution data only when necessary for event analysis, thus optimizing the balance between data quality and resource utilization.
2Loss of energy
If data is published at low resolution to conserve resources, then bandwidth and storage usage are optimized, but data integrity and event analysis capability are compromised
Solution Approach 1:
The system performs preliminary event detection and assessment before triggering high-resolution data publication. By monitoring operational parameters and identifying potential events in advance, the system prepares to switch to high-resolution mode, ensuring that critical data is captured with appropriate resolution while maintaining resource efficiency during normal operations.
Solution Approach 2:
The system uses feedback from event detection mechanisms to adjust data publication resolution. When events are detected or analysts request detailed analysis, the system receives feedback signals that trigger high-resolution data publication for relevant variables, thereby maintaining data integrity when needed while optimizing resource usage during normal operations.
3Measurement precision
If high-resolution data is published for all variables, then event analysis effectiveness is improved, but system complexity and processing load increase
Solution Approach 1:
The system applies different data publication qualities to different variables based on their relevance to current events. Instead of uniformly publishing high-resolution data for all variables, the system identifies and prioritizes relevant variables associated with detected events, publishing high-resolution data only for those specific variables while maintaining lower resolution for others, thus reducing system complexity while preserving event analysis capability.
Data Source
AI summary
Systems and methods described herein use context-adaptive resolution to achieve lossless transfer of critical industrial control system data. Bandwidth and storage optimization is achieved for time-series data generated or collected by an industrial control system and transmitted through a network to a remote historian system. The control system may be implemented through one or more embedded historians and local historians. The control system continuously processes input data, computes output variables, and generates an event trigger when a set of variables meet pre-defined set of conditions. When there are no active event triggers, the control system publishes time-series data for a pre-defined set of variables at a lower resolution to conserve bandwidth and storage. When an event trigger is activated, the control system publishes recent and current time-series data for a subset of the variables to subscribers at a higher resolution.