Disturbance Source Positioning via Node Group Causality Analysis
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Solution Overview
Problem
In industrial plants, identifying the root cause of loop oscillations caused by disturbance sources is time-consuming and labor-intensive, especially when multiple sources interact, leading to plant-wide oscillations that affect equipment operation and product quality.
Innovation Solution
A disturbance source positioning method that groups nodes based on oscillation features, establishes in-group causality, and selects candidate groups to identify disturbance sources, reducing computational complexity and manpower required for diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual detection methods are used to identify disturbance sources, then measurement precision can be maintained, but loss of time and loss of substance (manpower) increase significantly
Solution Approach 1:
The patent segments the complex disturbance source identification problem into distinct processing stages: signal acquisition from multiple nodes, oscillation feature extraction, node grouping based on oscillation characteristics, in-group causality establishment, and candidate group selection. This segmentation enables parallel processing of different signal streams and features, dramatically reducing detection time while maintaining identification accuracy through systematic analysis of each segment.
Solution Approach 2:
The patent replaces manual mechanical detection methods with automated computational analysis systems. The system automatically acquires signals from multiple nodes, extracts oscillation features, groups nodes, establishes causality relationships, and identifies disturbance sources through algorithmic processing. This substitution eliminates manual intervention, reducing both detection time and manpower requirements while maintaining or improving identification precision through consistent automated analysis.
2Measurement precision
If comprehensive analysis of all nodes is performed to ensure accurate disturbance source positioning, then measurement precision improves, but device complexity and computation burden increase
Solution Approach 1:
The patent segments all nodes into multiple groups based on their oscillation features, such that nodes within each group exhibit similar oscillation characteristics. This segmentation reduces the computational complexity by enabling group-level analysis rather than individual node analysis. The in-group causality establishment further segments the analysis focus to specific candidate groups, maintaining positioning accuracy while significantly reducing the overall computational burden compared to analyzing all nodes individually.
Solution Approach 2:
The patent performs partial analysis by focusing computational resources on candidate groups identified through in-group causality establishment, rather than conducting exhaustive analysis of all nodes. The method selectively applies detailed disturbance source positioning algorithms only to these candidate groups, achieving accurate disturbance source identification with reduced computation complexity by avoiding unnecessary analysis of nodes that are unlikely to be disturbance sources.
3Measurement precision
If multiple disturbance sources are analyzed simultaneously in interconnected loops, then complete disturbance source identification is achieved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the detection task by first grouping nodes based on oscillation features, then establishing in-group causality to identify candidate groups, and finally performing disturbance source positioning within each candidate group. This segmented approach breaks down the complex task of identifying multiple disturbance sources in interconnected loops into manageable stages, reducing the overall difficulty of detection and measurement while ensuring complete identification through systematic progression through each segment.
Solution Approach 2:
The patent introduces intermediary structures including node groups and in-group causality relationships as intermediate steps between raw signal data and final disturbance source identification. These intermediaries organize and structure the complex data from multiple interconnected loops, making the detection and measurement process more systematic and manageable. The intermediary causality analysis helps disentangle the interactions between multiple disturbance sources, reducing detection difficulty while maintaining complete identification.
Data Source
AI summary
A disturbance source positioning method for positioning disturbance sources in a system including a plurality of nodes is provided. The method includes the following steps: grouping the plurality of nodes into a plurality of node groups based on an oscillation feature; establishing an in-group causality of the plurality of node groups based on a successive order of a coherent oscillation component; selecting at least one candidate group from the plurality of node groups based on the in-group causality; and positioning at least one disturbance source node in each candidate group.