Correlation Destruction Pattern Analysis for Cross-Device Failure Detection
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Solution Overview
Problem
Existing system analysis methods struggle to determine failure causes when there are changes in the correlation model or when devices of the same type but different are involved, as they require identical correlation models and failure locations for accurate determination.
Innovation Solution
A system analysis device that generates aggregated destruction patterns by grouping correlation destruction patterns of the same type and calculates similarity between these patterns and new detection results, allowing for versatile state detection even with changes in the correlation model or different devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correlation destruction patterns are compared to determine failure causes, then failure cause determination accuracy is improved, but the method becomes inapplicable when there are changes in the correlation model or when different devices of the same type are involved
Solution Approach 1:
The patent creates a reference correlation destruction pattern from historical failure data and uses it as a template to compare against new failure patterns. Instead of requiring exact matches between specific devices, the system copies the pattern-matching approach to identify similar failure modes across different devices and correlation models, enabling versatile failure cause determination while maintaining accuracy through pattern similarity comparison.
2Measurement precision
If the system requires identical correlation models for failure analysis, then determination accuracy is improved, but the system cannot handle distributed processing scenarios or model changes
Solution Approach 1:
The patent develops a universal failure analysis method that works across different correlation models and device configurations. By extracting failure patterns from historical data and comparing them with new failures using similarity metrics, the system achieves multi-functionality that handles both identical and changed correlation models, as well as different devices of the same type, without requiring exact model matching.
3Measurement precision
If specific failure locations must match for pattern comparison, then determination precision is improved, but the method fails when different devices experience similar failures
Solution Approach 1:
The patent transforms the comparison approach by changing from exact location matching to pattern similarity comparison. Instead of requiring identical failure locations, the system uses parameters such as correlation destruction patterns, metric relationships, and failure characteristics to identify similar failures across different devices. This parameter transformation enables both precision through pattern matching and versatility across different device instances.
Data Source
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AI summary
In state detection of a system using a correlation destruction pattern, the versatility of the correlation destruction pattern is improved. A system analysis device (100) includes a correlation destruction pattern storage unit (113), an aggregated destruction pattern generation unit (104), and a similarity calculation unit (105). The correlation destruction pattern storage unit (113) stores a plurality of correlation destruction patterns (123) each of which is a set of correlations in which correlation destruction has been detected among correlations of pairs of metrics in a system. The aggregated destruction pattern generation unit (104) generates an aggregated destruction pattern (124) which is obtained by aggregating correlation destruction patterns (123) of the same type among the plurality of correlation destruction patterns (123). The similarity calculation unit (105) calculates and outputs a similarity between the aggregated destruction pattern (124) and a newly-detected correlation destruction pattern (123).