Home Appliance Trouble Diagnosis Using Server-Learned Detection Patterns

A data learning server is introduced as an intermediary component between home appliances and users. This server collects production information and operation information, generates learning models through machine learning algorithms, and provides trouble diagnosis services. By placing the complex learning model generation and update operations on a centralized server rather than in individual appliances, the system achieves high diagnostic accuracy while keeping individual device complexity low.

The data learning server continuously collects and stores operation information from home appliances in advance, and pre-generates learning models using this accumulated data. When a trouble diagnosis is needed, the system can quickly query pre-processed information and apply existing learning models, avoiding time-consuming data collection and model generation at the moment of diagnosis. This preliminary preparation significantly reduces data processing time while maintaining high trouble detection capability.

The system implements automated trouble diagnosis where the data learning server independently collects operation information, generates learning models, estimates trouble items, and provides diagnosis results without requiring user intervention in the complex processes. Users simply need to interact with a simple interface to request diagnosis and receive results, while the system handles all complex operations automatically. This self-service approach maintains high diagnostic precision while preserving ease of operation for users.