AI Learning Models for Home Appliance Trouble Detection

The patent replaces rule-based diagnostic systems with AI-based machine learning systems. The server collects operation information from home appliances and uses trained learning models to diagnose troubles, substituting the mechanical rule-based approach with intelligent algorithms that continuously learn from data, thereby significantly improving diagnosis accuracy while managing system complexity through modular architecture.

The patent transforms the diagnostic approach by changing from fixed rule parameters to dynamic learning parameters. The system collects multiple types of operation information (temperature, pressure, current, vibration) and uses these varying parameters as inputs to the learning model, allowing the diagnosis system to adapt to different appliance states and improve accuracy through data-driven parameter analysis.

The patent segments the AI-based diagnostic system into distinct functional modules: an information collection unit that gathers operation data from appliances, a server unit that trains and stores learning models, and a diagnosis unit that applies models to detect troubles. This segmentation reduces implementation complexity by allowing each module to be developed and maintained independently while ensuring reliable diagnosis through coordinated operation.

The patent introduces a server as an intermediary between home appliances and users. The server collects operation information from multiple appliances, trains learning models using aggregated data, and provides diagnostic services to both appliances and users. This intermediary approach distributes system complexity to the server infrastructure while keeping individual appliances simple, thereby improving overall reliability without burdening end devices.

The patent implements preliminary action by continuously collecting and storing operation information from home appliances in the server's database. Instead of processing data in real-time when diagnosis is needed, the system pre-processes and accumulates training data over time, allowing learning models to be trained on comprehensive datasets without delaying actual diagnostic operations, thus improving adaptability while minimizing time loss during critical moments.

The patent ensures continuous useful action by maintaining ongoing data collection from appliances and continuous model training on the server. The learning models are repeatedly trained with new operation information, continuously improving detection adaptability. This continuous process transforms what could be time-consuming batch processing into an ongoing background operation that enhances system intelligence without interrupting normal appliance operation or diagnosis services.