Contextual Event Sequence Analysis for System Failure Diagnosis
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Solution Overview
Problem
Complex electronic systems generate vast event logs that are difficult to analyze manually, making it challenging for operators to identify the root cause of failures and their propagation, leading to inefficiencies in fault diagnosis and mitigation.
Innovation Solution
A computerized system employing machine learning algorithms and automaton models to extract patterns from event records, generate event corpora, encode latent representations, cluster failure events, and order automaton models temporally to diagnose system failures and prevent their propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of event logs is used, then system operators can identify root causes of failures, but the process becomes inefficient and time-consuming due to the vast amount of data
Solution Approach 1:
The patent introduces an intermediary system comprising an event pattern extractor, corpus generator, and automaton model generator that mediates between the raw event logs and the operator. This intermediary automatically processes the vast event data, extracts meaningful patterns, and presents structured failure propagation paths, thereby resolving the contradiction between handling large data volumes and maintaining diagnostic efficiency.
Solution Approach 2:
The patent replaces the manual mechanical analysis process with an automated computational system. Instead of operators manually examining event logs, the system uses algorithmic processing to extract patterns, generate corpora, and construct automaton models that automatically identify root causes and propagation paths, significantly improving productivity while reducing time loss.
2Productivity
If automated pattern extraction and analysis is implemented, then fault diagnosis efficiency improves, but the device complexity increases due to multiple processing components
Solution Approach 1:
The patent segments the complex analysis task into distinct functional modules: an event pattern extractor that identifies recurring patterns, a corpus generator that structures extracted patterns, and an automaton model generator that creates formal models of failure propagation. This segmentation manages complexity by organizing functions into separate, manageable components while maintaining high diagnostic efficiency.
Solution Approach 2:
The patent creates a universal event analysis framework that can handle multiple types of system events and failure modes through a single integrated system. The event pattern extractor, corpus generator, and automaton model generator work together as a multi-functional system that processes diverse event data uniformly, improving productivity without proportionally increasing complexity.
Data Source
AI summary
Systems and methods for contextual event sequence analysis of system failure that analyzes heterogeneous system event record logs are disclosed. The disclosure relates to analyzing event sequences for system failure in ICT and other computerized systems and determining their causes and propagation chains.