Event Correlation Matrix for Complex System Maintenance
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Solution Overview
Problem
Current system maintenance methods face challenges in accurately correlating data from disparate sources, leading to incomplete information and inefficient decision-making due to manual errors, inaccurate timestamp information, and varying data fidelity, which hinders effective preventive maintenance.
Innovation Solution
A computer-implemented method that matches event information across multiple data streams from various sources, scoring imprecise event generation information to improve match quality, using a matrix representation and induced subgraphs to identify relevant data sources and calculate time proximity, thereby providing actionable insights for system maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to match event information from multiple data sources, then human judgment can be applied to assess data quality, but the process becomes error-prone and time-consuming
Solution Approach 1:
The patent replaces manual inspection with an automated computer-implemented method that uses algorithms to match event information across multiple data sources. The system automatically scores imprecise event generation information and correlates events from disparate sources, eliminating human error and time consumption while maintaining or improving correlation accuracy through systematic processing of timestamp proximity and data fidelity metrics.
2Productivity
If automatic matching techniques are used to correlate events across data sources, then processing speed increases, but timestamp inaccuracies cause failed correlations
Solution Approach 1:
The patent introduces a scoring mechanism that evaluates the quality of event matching by considering timestamp proximity, data source fidelity, and event characteristics. Instead of relying solely on exact timestamp matches, the system calculates scores that account for acceptable time variations and data quality differences, thereby improving correlation reliability while maintaining automated processing speed.
3Loss of information
If multiple data sources are integrated to provide comprehensive system information, then the completeness of maintenance information improves, but the complexity of correlating events across sources increases
Solution Approach 1:
The patent segments the complex correlation task into distinct processing steps: retrieving event information from multiple data sources, scoring each event based on quality metrics, filtering events by threshold scores, and correlating only the high-quality events. This segmentation reduces the overall complexity by breaking down the integration challenge into manageable, automated components that can be processed systematically.
4Loss of information
If low fidelity data is included in the analysis, then the comprehensiveness of available information increases, but the accuracy of event matching decreases
Solution Approach 1:
The patent transforms low fidelity data into usable information by applying a scoring mechanism that evaluates each event's reliability based on multiple parameters including data source fidelity, timestamp accuracy, and event characteristics. Events from low fidelity sources are not discarded but are assigned lower scores, allowing the system to weigh them appropriately against higher fidelity events, thus maintaining completeness while preserving accuracy through differential weighting.
Data Source
AI summary
A computer-implemented method of improving maintenance of a complex system, the complex system having a plurality of components, the method involving: preparing data across a plurality of data streams from a plurality of data sources; generating a matrix representation of the data; calculating a time proximity of the data; calculating a plurality of corresponding cell values of the matrix representation; matching event information across the plurality of data streams from a plurality of data sources, the plurality of data sources corresponding to the plurality of components, wherein at least one data stream of the plurality of data streams has at least one of low fidelity data and imprecise event generation information; and scoring the imprecise event generation information across the plurality of data streams, thereby providing a score indicating a match quality of the imprecise event generation information.