Failure Analysis Device Using Learning Models
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Solution Overview
Problem
Current methods for identifying the cause of failures in communication systems are time-consuming and inefficient, as they require manual verification of multiple potential causes, making it impractical to promptly address failures and implement countermeasures.
Innovation Solution
A failure analysis device and method that uses a learning model to discriminate the occurrence of failures based on cause attributes and identify corresponding countermeasures, utilizing training data to generate models such as heterogeneous mixture learning, decision trees, or discriminants to quickly determine the cause and necessary actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of each candidate cause is performed, then the accuracy of failure cause identification can be ensured, but the time required to identify the cause increases significantly
Solution Approach 1:
The system performs preliminary learning and analysis by collecting failure data and candidate causes in advance, generating a ranked list of potential causes before actual failure analysis is needed. This preliminary preparation enables rapid identification when failures occur without sacrificing accuracy.
Solution Approach 2:
The patent replaces manual verification with an automated information processing system that uses learning models and data analysis to identify failure causes. This substitution of mechanical/manual processes with automated computational methods resolves the contradiction between thorough verification and time efficiency.
2Reliability
If comprehensive data collection and analysis are performed to accurately identify failure causes, then the reliability of failure analysis improves, but the device complexity increases
Solution Approach 1:
The failure analysis device integrates multiple functions including data collection, candidate cause generation, learning model application, and result output into a single unified system. This multi-functionality approach improves reliability by ensuring comprehensive analysis while managing complexity through functional integration rather than separate components.
Solution Approach 2:
The patent introduces a learning model as an intermediary between raw failure data and cause identification results. This intermediary component processes and structures the data, enabling reliable analysis without requiring direct complex interactions between all system components, thus managing system complexity.
3Productivity
If automated learning models are used to quickly identify failure causes, then productivity increases, but the precision of cause identification may be compromised
Solution Approach 1:
The system performs preliminary learning during normal operation by collecting failure data and analyzing patterns, so that when actual failures occur, the automated model is already trained and ready to quickly identify causes with high precision. This preliminary preparation enables both speed and accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the results of automated failure analysis are used to continuously refine and improve the learning models. This feedback loop ensures that automated identification maintains high precision by learning from actual failure cases and improving over time.
Data Source
AI summary
A failure analysis device 10 is provided with an identification unit 11 that discriminates whether a predetermined failure has occurred on the basis of a learning model for discriminating the presence or absence of an occurrence of the predetermined failure learned by using a cause attribute which is associated with a cause of the predetermined failure and on the basis of a value of the attribute, and that identifies the cause of the predetermined failure discriminated to have occurred and countermeasures therefor.