Electronic Apparatus Error Prediction Using Component Correlation
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Solution Overview
Problem
Existing electronic apparatuses lack efficient error prediction and prevention methods, leading to unreliable error handling and increased costs due to varied service outcomes based on engineer experience and potential secondary problems from incorrect diagnoses.
Innovation Solution
An electronic apparatus equipped with a processor that collects state data from components, identifies potential errors, and performs error-related operations by adjusting parameters of correlated components using AI models to predict and prevent errors, providing a user interface for user input and guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error handling relies on service engineer experience and capability, then error resolution may be achieved, but service quality varies and reliability is compromised
Solution Approach 1:
The electronic apparatus autonomously detects errors, identifies correlated components, and executes error-related operations without requiring service engineer intervention. The system self-diagnoses by analyzing state data from multiple components and automatically performs corrections, eliminating variability in service quality while maintaining high reliability
Solution Approach 2:
The system continuously monitors state data from components and uses machine learning models to predict potential errors before they occur. By establishing feedback loops that track component correlations and update predictions based on actual error occurrences, the system achieves consistent, reliable error handling that is independent of human expertise
2Ease of manufacture
If previously defined error guides are used, then standardization is improved, but the guide may cause secondary problems from wrong diagnosis
Solution Approach 1:
Instead of using fixed, previously defined error guides, the system dynamically adjusts diagnostic parameters based on real-time state data from multiple components. The machine learning model continuously updates component correlation relationships and error predictions by analyzing actual operational data, enabling accurate diagnosis that adapts to varying system conditions while maintaining standardization
Solution Approach 2:
The system performs preliminary error prediction by analyzing state data and identifying potential errors before they actually occur. By proactively detecting at-risk components and predicting likely failures, the system can prepare appropriate error-related operations in advance, preventing wrong diagnoses and secondary problems that would result from reactive, rule-based error handling
3Reliability
If error prediction and prevention is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The processor is designed to perform multiple functions: it processes state data from components, executes machine learning models for error prediction, identifies correlated components, and performs error-related operations. By consolidating these diverse functions into a single multi-functional processor, the system achieves sophisticated error prediction capability without proportionally increasing device complexity
Solution Approach 2:
The system merges the error detection, analysis, prediction, and correction functions into an integrated error management system. By combining state data collection from multiple components with machine learning-based prediction and automatic error handling in a unified architecture, the system achieves high reliability while minimizing the complexity increase that would result from separate, dedicated subsystems
Data Source
AI summary
Disclosed is an electronic apparatus including a processor, and a memory configured to store instructions executable by the processor in which the processor is configured to: obtain state data about operations of a plurality of components in the electronic apparatus, identify a state error of a first component among the plurality of components based on the obtained state data, obtain error data about a correlation between the identified state error of the first component and a second component causing the state error among the plurality of components, identify possibility of occurrence of the state error of the first component based on the obtained state data and the obtained error data, and perform an error-related operation for the second component correlating with the state error of the first component.