Matchset-Based Root Cause Analysis for Complex Systems
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Solution Overview
Problem
Determining the root cause of problems in complex systems is challenging due to high symptom density, uncertainty in available information, and insufficient data to disambiguate between potential root causes, making it impossible to determine the actual root cause with absolute certainty.
Innovation Solution
Implementing a matchset-based automatic root cause analysis (ARCA) that refines multiple matches for a given symptom set based on relationships between symptoms, identity, and confidence levels, using an indexing mechanism to efficiently process and organize potential fault scenarios, and associating probabilities with output rather than input to generate a set of likely root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional root cause analysis methods are used, then the system can identify potential root causes, but the number of potential root causes increases due to high symptom density and fault propagation
Solution Approach 1:
The patent segments the fault diagnosis process into multiple stages: symptom collection, fault scenario generation, matchset formation, and refinement. By dividing the complex diagnosis task into manageable segments, the system can handle high symptom density systematically and reduce the number of potential root causes through structured analysis.
Solution Approach 2:
The patent generates more fault scenarios than strictly necessary (excessive action) to ensure comprehensive coverage of potential root causes, then uses refinement mechanisms to filter out incorrect ones. This approach ensures that even with high symptom density and fault propagation, the actual root cause is included in the final results.
2Reliability
If more fault scenarios are generated to cover all possibilities, then the completeness of root cause analysis improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining fault scenarios and their associated symptoms before actual diagnosis occurs. This allows the system to quickly match observed symptoms against pre-prepared fault scenarios during diagnosis, reducing processing time while maintaining completeness through comprehensive pre-defined scenarios.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines the set of potential root causes based on observed symptoms and their relationships. By feedback-driven refinement, the system maintains reliability through comprehensive analysis while reducing processing time by eliminating obviously incorrect scenarios early in the process.
3Measurement precision
If the system considers all possible fault scenarios, then the accuracy of root cause identification improves, but the uncertainty from insufficient or incorrect telemetry increases
Solution Approach 1:
The patent changes the parameters of fault scenario evaluation by introducing confidence levels and probability weights that adjust based on telemetry reliability. When telemetry is insufficient or incorrect, the system modifies the evaluation parameters to account for this uncertainty, maintaining accuracy while acknowledging information losses.
Solution Approach 2:
The patent introduces intermediary elements such as confidence levels and probability distributions that mediate between the observed symptoms and the final root cause identification. These intermediaries account for telemetry reliability issues by providing a layered approach that separates raw data quality from final diagnostic conclusions.
4Ease of operation
If the system refines matchsets to reduce potential root causes, then the clarity of diagnosis improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent merges multiple refinement criteria into a unified matchset-based approach that simultaneously handles symptom relationships, fault scenario probabilities, and telemetry reliability. By combining these aspects into a single integrated process, the system achieves clear diagnosis while reducing the apparent complexity through consolidation.
Data Source
AI summary
A set of observed symptoms of a physical system is received. The set of observed symptoms is matched with a database of fault scenarios to generate a plurality of matched fault scenarios, wherein the database of fault scenarios comprises combinations of symptoms and corresponding root cause identifiers. The plurality of matched fault scenarios are refined. It is determined whether a first fault scenario of the plurality of matched fault scenarios is to be subsumed by a second fault scenario. In response to a determination that the first fault scenario of the plurality of matched fault scenarios is to be subsumed by a second fault scenario, the second fault scenario is included in the plurality of matched fault scenarios, the first fault scenario is deprioritized, or both. The plurality of matched fault scenarios as potential root causes is output.