Causal Network Models for Automated Diagnostic Root Cause Analysis
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Solution Overview
Problem
Conventional diagnostic techniques, such as expert systems and knowledge bases, face challenges in accurately identifying problem causes due to incomplete failure data and subjective interpretation, leading to incorrect fault isolation and false diagnoses in complex systems.
Innovation Solution
A method for performing diagnostics that generates a topological relationship between applications and systems, using causal network models to identify relationships and determine relevant models for diagnostic processing, thereby facilitating accurate problem identification and root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If expert systems use rule-based deterministic approaches for diagnosis, then the diagnostic process is straightforward and automated, but the accuracy of problem identification deteriorates due to incomplete failure data and difficulty in determining appropriate rules
Solution Approach 1:
The patent replaces the mechanical rule-based deterministic system with a probabilistic causal network model. Instead of using fixed if-then rules that fail with incomplete data, the system uses probabilistic relationships between variables to infer causes, allowing accurate diagnosis even when failure data is incomplete by reasoning about causal connections rather than matching predetermined rules.
Solution Approach 2:
The patent changes the fundamental parameter of diagnostic reasoning from deterministic (0 or 1) to probabilistic (0 to 1 continuous values). By representing causal relationships as probabilistic dependencies rather than absolute rules, the system can handle incomplete data gracefully and maintain diagnostic accuracy through probability propagation through the causal network.
2Ease of operation
If knowledge bases use unstructured tribal knowledge for self-service diagnosis, then users can access diagnostic information, but the time required to read, understand, and interpret knowledge deteriorates significantly
Solution Approach 1:
The patent replaces the manual information retrieval process with automated probabilistic inference. Instead of requiring users to read and interpret unstructured knowledge bases, the system automatically queries the causal network model to infer the most likely causes and recommended actions, providing structured diagnostic results instantly without human interpretation time.
Solution Approach 2:
The patent enables the diagnostic system to serve itself by automatically performing the diagnostic reasoning that would otherwise require user intervention. The causal network model autonomously processes the diagnostic task, eliminating the need for users to manually search through knowledge bases while still providing personalized diagnostic results.
3Adaptability or versatility
If diagnostic systems use subjective user interpretation to identify problems, then users can provide contextual information, but the reliability of fault isolation deteriorates due to incorrect problem isolation and false faults
Solution Approach 1:
The patent replaces subjective human interpretation with objective probabilistic inference based on causal networks. The system maintains adaptability by allowing the causal network to be trained on diverse failure modes and contextual information, while eliminating subjectivity by using mathematical probability calculations to determine the most likely causes, thereby improving reliability through consistent, repeatable reasoning.
Data Source
AI summary
Techniques for performing diagnostics are described. In one embodiment, in response to an alert or a request to perform diagnostics, a topological relationship is generated comprising a set of applications and a set of systems determined based upon information in the alert or request. The topological relationship encapsulates relationships between the set of applications and the set of systems. In one embodiment, a set of causal network models to be used for performing the diagnostics is determined based upon the applications and systems in the topological relationship.