Component-Based Learning for Cross-Apparatus Fault Correction
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Solution Overview
Problem
Existing machine learning models for predicting apparatus defects are inefficient when apparatus configurations differ, requiring extensive time to collect sufficient learning data and cannot adapt to varying configurations of the same basic form of home appliances.
Innovation Solution
A machine learning system that selects and utilizes learning data based on apparatus configuration information, training a model to estimate corrective actions by incorporating data from similar constituent components across different apparatus types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning data is collected for each specific apparatus type, then prediction accuracy for that apparatus type can be improved, but the time required to collect sufficient learning data increases significantly
Solution Approach 1:
The patent combines learning data from multiple apparatus types that share common constituent components into a unified learning dataset. By merging data across different apparatus types (e.g., refrigerators, air conditioners, washing machines) that use the same components (compressors, motors, sensors), the system accumulates sufficient learning data faster while maintaining prediction accuracy through component-based generalization.
Solution Approach 2:
The patent creates a universal learning model that can predict failures across multiple apparatus types by focusing on common constituent components. The model is designed to handle diverse apparatus types simultaneously, making the learning system multi-functional and applicable to various devices sharing the same components, thereby reducing the need for separate data collection for each apparatus type.
2Adaptability or versatility
If a learning model is trained using data from apparatus types with different configurations, then the model's adaptability improves, but the precision of fault prediction for specific apparatus configurations deteriorates
Solution Approach 1:
The patent segments the learning data and model training by constituent components rather than by apparatus types. Each common component (compressor, motor, sensor) is treated as an independent learning unit. This segmentation allows the model to adapt to different apparatus configurations while maintaining precision by focusing on component-specific failure patterns rather than apparatus-specific variations.
Solution Approach 2:
The patent applies local quality by tailoring the prediction precision to specific component types within the broader apparatus system. While the model is universally applicable across apparatus types, it maintains high precision for each specific component by learning component-specific failure patterns from aggregated data, ensuring that each component's prediction accuracy is optimized independently.
Data Source
AI summary
A training unit is configured to perform training of the learning model for estimating corrective action content from a type of fault for each type of apparatus based on the data of corrective action for the fault. The training unit is configured to: perform training of the learning model for estimating the corrective action content from the type of fault for an apparatus of a first type. This training uses, as learning data, data of corrective action for the fault of the apparatus of the first type, and data of corrective action for the fault related to a common constituent component of an apparatus of a different type that shares the common constituent component with the fault component of the apparatus of the first type.