Multi-Node Stream Processing for Holistic Asset Operation Control
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Solution Overview
Problem
Industrial process control systems typically operate each component independently, without considering the holistic view of other components or underlying conditions, leading to inefficient or undesirable states, as they rely on real-time data from individual sensors and preset thresholds without accounting for the collective performance of assets in a network.
Innovation Solution
A stream-based processing system that receives and analyzes data from multiple nodes in real-time or near real-time, using analysis engines to model underlying conditions, apply fuzzy logic, and determine remedial actions, which can be automatically taken or require user approval, to optimize the operation of assets within a multi-node network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time data processing is implemented at individual component level, then response speed is improved, but system-wide optimization capability deteriorates
Solution Approach 1:
The system divides data processing into two segments: real-time stream-based processing at the component level for immediate response, and holistic analysis at the server level for system-wide optimization. This segmentation allows both fast local responses and comprehensive global optimization to coexist.
Solution Approach 2:
The server acts as an intermediary that receives stream-based data from multiple components, performs holistic analysis considering system-wide conditions, and sends optimized control commands back to components. This intermediary enables system-wide optimization without compromising individual component response speed.
2Productivity
If holistic system analysis is performed, then operational efficiency is improved, but data processing complexity increases
Solution Approach 1:
Processing complexity is segmented between components (simple threshold-based monitoring) and server (holistic stream-based analysis). This division allows complex system-wide analysis to be performed only where necessary, maintaining operational efficiency without overwhelming individual components.
Solution Approach 2:
The server provides universal stream-based processing capabilities that handle multiple data sources and analysis functions centrally. This multi-functional approach consolidates complex processing in one location, reducing overall system complexity while maintaining holistic analysis benefits.
3Device complexity
If component-independent control is used, then system simplicity is maintained, but overall process optimization deteriorates
Solution Approach 1:
The system merges component-level stream-based data with system-wide contextual information at the server level. This combining enables holistic analysis that considers interdependencies between components, achieving process optimization while maintaining relative system simplicity through modular architecture.
Data Source
AI summary
A system includes a first asset disposed in an industrial environment configured to perform one or more operations, a second asset disposed in the industrial environment, and a server device communicatively coupled to the first asset and the second asset. The server device is configured to receive a first set of stream-based data from the first asset, receive a second set of stream-based data from the second asset, wherein the first set of stream-based data and the second set of stream-based data are received in real time or near real time, determine whether the one or more operations are within a threshold based on a comparison of the first set of stream-based data with respect to the second set of stream-based data, and send a command to the first asset or the second asset in response to the one or more operations being outside the threshold.