Ternary Fault Scenario Matching for Automatic Root Cause Analysis
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Solution Overview
Problem
Complex systems face challenges in determining the root cause of faults due to numerous sources and component dependencies, with traditional methods relying on statistical correlation and large training datasets, which are costly and impractical, and fail to incorporate system knowledge effectively.
Innovation Solution
The implementation of automatic root cause analysis using ternary fault scenario representation, where symptoms are restricted to true, false, or unknown values, allowing for efficient matching and encoding of system principles, dependencies, and causations without requiring extensive statistical correlation or large datasets, thereby reducing storage costs and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If statistical correlation methods are used to find root causes, then automated root cause analysis can be performed, but the reliability is low and large training datasets are required which are expensive and impractical
Solution Approach 1:
The patent changes the fundamental parameters of fault representation from continuous statistical values to discrete ternary states (true, false, unknown). This transformation enables exact matching logic to replace probabilistic statistical correlation, achieving both automation and high reliability without requiring large training datasets. The ternary fault scenario representation allows the system to deterministically identify root causes through logical deduction rather than statistical inference.
2Ease of operation
If traditional fault analysis methods are used, then root cause determination can be performed, but the complexity increases due to numerous faults and component dependencies
Solution Approach 1:
The patent segments the complex fault analysis problem into distinct ternary state components for each symptom and fault. By dividing the continuous fault space into discrete true/false/unknown states, the system simplifies the analysis of numerous interconnected faults. Each fault scenario becomes a manageable combination of ternary values rather than a complex continuous variable, making the determination process tractable even for large-scale systems.
Solution Approach 2:
The patent transforms the complex continuous fault parameters into discrete ternary parameters, reducing the complexity of analyzing numerous interconnected faults. This parameter discretization converts an intractable continuous optimization problem into a manageable discrete logic problem, enabling easy operation despite high system complexity.
3Productivity
If machine learning techniques are used to recognize failure scenarios, then automated analysis can be performed, but extensive training datasets are required which are expensive and impractical
Solution Approach 1:
The patent changes the approach from data-intensive machine learning to logic-based ternary matching by transforming fault parameters into discrete true/false/unknown states. This enables automated failure scenario recognition through deterministic logical deduction rather than statistical learning, achieving high productivity without requiring extensive training datasets.
Data Source
AI summary
A plurality of potential fault scenarios are accessed, wherein a given potential fault scenario of the plurality of potential fault scenarios has at least one corresponding root cause, and a representation of the given potential fault scenario comprises a don't care value. An actual fault scenario from telemetry received from a monitored system is generated. The actual fault scenario is matched against the plurality of potential fault scenarios. One or more matched causes are output as one or more probable root cause failures of the monitored system.