Low-Rank Network Diffusion for Causal Anomaly Inference
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Solution Overview
Problem
Diagnosing system faults in complex systems is challenging due to their size and complexity, making manual inspection of monitoring data infeasible, and existing automated methods fail to accurately model local propagation patterns of causal anomalies.
Innovation Solution
A computer-implemented method using a low-rank network diffusion model to identify functional modules impacted by causal anomalies and backtrack these anomalies by jointly clustering invariant and broken networks, narrowing the search space and accurately modeling local propagation patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated anomaly inference algorithms are developed to handle large-scale systems, then manual inspection becomes feasible and productivity improves, but the complexity of accurately modeling local propagation patterns increases
Solution Approach 1:
The patent segments the complex system into functional modules and further into invariant subspaces using clustering algorithms. This segmentation allows the anomaly inference process to focus on local propagation patterns within modules rather than analyzing the entire system globally, thereby managing complexity while maintaining diagnostic productivity.
Solution Approach 2:
The patent introduces an intermediary low-rank network diffusion model that bridges the gap between monitoring data and causal anomaly identification. This intermediary model captures local propagation patterns and transforms complex system-wide analysis into manageable module-level inference, resolving the contradiction between automation efficiency and modeling complexity.
2Measurement precision
If the entire monitoring data is manually inspected to identify causal anomalies, then measurement precision improves, but the loss of time increases significantly
Solution Approach 1:
The patent extracts causal anomalies from large volumes of monitoring data by identifying invariant subspaces and focusing analysis on impaired functional modules. This extraction approach maintains measurement precision by concentrating computational resources on relevant data portions while dramatically reducing the time required compared to manual inspection of entire datasets.
Solution Approach 2:
The patent performs preliminary clustering and identification of invariant subspaces before conducting anomaly inference. This preliminary action organizes monitoring data into structured functional modules, enabling faster and more accurate causal anomaly detection without requiring manual inspection of raw data, thus reducing diagnostic time while preserving precision.
3Productivity
If existing automated methods are used for causal anomaly inference, then productivity improves, but measurement precision deteriorates due to inability to model local propagation patterns
Solution Approach 1:
The patent applies local quality by modeling propagation patterns specifically within impaired functional modules rather than using uniform system-wide models. The low-rank network diffusion model adapts to local characteristics of each module, capturing specific propagation patterns that existing automated methods miss, thereby improving measurement precision while maintaining automated productivity.
Solution Approach 2:
The patent changes the modeling parameters by using low-rank network diffusion models that capture local propagation dynamics differently from existing methods. This parameter change enables the system to maintain automated processing speed while improving causal anomaly inference accuracy through better representation of local propagation patterns within functional modules.
Data Source
AI summary
A computer-implemented method for diagnosing system faults by fine-grained causal anomaly inference is presented. The computer-implemented method includes identifying functional modules impacted by causal anomalies and backtracking causal anomalies in impaired functional modules by a low-rank network diffusion model. An invariant network and a broken network are inputted into the system, the invariant network and the broken network being jointly clustered to learn a degree of broken severities of different clusters as a result of fault propagations.