Event Root Cause Analysis Rule Generation
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Solution Overview
Problem
Conventional failure analysis systems require pre-defined IF-THEN rules to analyze failures, making it difficult for administrators to anticipate and prepare for potential failures, increasing workload and susceptibility to human error.
Innovation Solution
A computer system analyzes event occurrence logs to automatically create new rules by identifying event groups with common causes, allowing for dynamic rule creation and storage for future analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-defined IF-THEN rules are used for failure analysis, then analysis accuracy is improved, but device complexity and operational burden increase
Solution Approach 1:
The system automatically extracts rules from event occurrence logs without requiring manual definition by administrators. The rule extraction unit processes past event data to generate IF-THEN rules dynamically, enabling the system to serve itself rather than requiring human expertise for rule creation and maintenance.
Solution Approach 2:
The system uses feedback from actual event occurrence logs to refine and update analysis rules. By continuously learning from past failures and their outcomes, the system improves its analysis accuracy over time through iterative rule extraction and validation based on real operational data.
2Reliability
If pre-defined IF-THEN rules are used for failure analysis, then analysis reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically extracts rules from event occurrence logs without requiring manual definition by administrators. The rule extraction unit processes past event data to generate IF-THEN rules dynamically, enabling the system to serve itself rather than requiring human expertise for rule creation and maintenance.
Solution Approach 2:
The system performs preliminary rule extraction from historical event data before actual failure analysis is needed. By pre-processing past event occurrence logs to create a library of analysis rules, the system prepares analytical capabilities in advance, eliminating the need for administrators to manually create rules before each analysis task.
3Adaptability or versatility
If manual rule creation is performed, then adaptability to specific failure modes is improved, but productivity deteriorates
Solution Approach 1:
The system automatically extracts rules from event occurrence logs without requiring manual definition by administrators. The rule extraction unit processes past event data to generate IF-THEN rules dynamically, enabling the system to serve itself rather than requiring human expertise for rule creation and maintenance.
Solution Approach 2:
The system changes the approach from static manual rule definition to dynamic automated rule generation. By transforming the rule creation process from a manual parameter-setting task to an automated data-extraction process, the system maintains adaptability while dramatically improving productivity through algorithmic rule synthesis from event patterns.
Data Source
AI summary
A computer analyzes the root cause of an event, which has occurred in any of multiple management-target apparatuses, based on one or more rules in a storage device, that denote an association between one or more condition events corresponding to one or more events capable of occurring in any of the multiple management-target apparatuses and a conclusion event, which is the cause in a case where the one or more condition events have occurred. The computer, based on an event occurrence log including contents and an occurrence date and time of an event, determines a first event group, which is multiple events presumed to occur as a result of the same cause, creates a new rule in which the multiple events of the first event group are the condition events and one event of the first event group is the conclusion event, and stores the created new rule.