Ternary Fault Scenario Matching for Automated Root Cause Analysis

The patent introduces an intermediary causal model that represents domain knowledge about component dependencies and failure propagation. This causal model acts as a mediator between the observed symptoms and the root cause identification process, replacing direct statistical correlation with knowledge-based reasoning. The causal model encodes expert knowledge about which components can cause failures in other components, enabling reliable automated analysis without relying on potentially misleading statistical correlations.

The patent applies preliminary action by pre-encoding causal knowledge and failure scenario patterns into the causal model before actual root cause analysis is needed. Instead of requiring large training datasets during operation, the system performs the learning and pattern recognition work in advance during model construction. The causal model is built using expert knowledge and historical failure data, then reused repeatedly for automated analysis without requiring additional training data.

The patent segments the complex set of failure alerts into organized failure scenarios within the causal model. Each scenario represents a coherent failure pattern with a specific root cause and associated symptoms. This segmentation transforms the overwhelming task of analyzing numerous individual alerts into a structured process of matching observed symptoms against predefined scenarios, dramatically improving productivity while maintaining clarity through organized categorization.