Appliance Failure Prediction Control With Delayed Maintenance Scheduling
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Solution Overview
Problem
Current home network systems lack an efficient method to predict and manage appliance failures, leading to unscheduled maintenance and increased costs, as they do not effectively consider user patterns and schedules in delaying or fixing predicted failures.
Innovation Solution
A method and apparatus that utilize AI-driven systems to receive prediction information about appliance failures, obtain repair service schedules, request maintenance information to delay failures, and control appliances to maintain normal operation, allowing for concurrent repair of multiple failures during a single visit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI-driven failure prediction is implemented, then reliability of appliance maintenance is improved, but device complexity increases
Solution Approach 1:
The patent introduces an AI server as an intermediary component that handles the complex failure prediction and maintenance scheduling tasks. The appliance itself remains relatively simple, while the AI server performs the sophisticated analysis of operation data, generates failure predictions, and determines optimal maintenance schedules. This distributes the complexity from the appliance to the external AI service.
Solution Approach 2:
The system enables self-service maintenance scheduling where the AI-driven system automatically analyzes appliance operation data, predicts potential failures, and schedules maintenance activities without requiring constant human intervention. The appliance monitors its own operational parameters and triggers maintenance requests based on predicted failure risks.
2Loss of time
If maintenance is delayed to align with repair service schedules, then loss of time for repair services is reduced, but reliability of appliance operation deteriorates
Solution Approach 1:
The system performs preliminary failure prediction by analyzing appliance operation data before actual failures occur. The AI server processes operation data to identify potential failure patterns and schedules maintenance activities in advance, allowing repairs to be performed during planned service windows rather than after failures occur, thus reducing unplanned downtime.
Solution Approach 2:
The maintenance scheduling is dynamic rather than static. The system continuously monitors appliance operation data and adjusts maintenance schedules based on real-time failure predictions. If the predicted failure risk changes or repair service availability changes, the schedule is automatically updated to optimize both reliability and time efficiency.
3Productivity
If multiple repairs are performed concurrently, then productivity of repair services is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple repair activities into coordinated service visits. The AI server analyzes multiple appliance failures and schedules them to occur during the same service window, allowing repair technicians to service multiple appliances in one visit. This consolidates what would otherwise be separate repair events into a single productive intervention.
Solution Approach 2:
The repair service system is designed with multi-functionality to handle diverse appliance types and failure modes within a single service framework. The AI server can predict failures across different appliance categories and coordinate comprehensive repair activities that address multiple issues simultaneously, making the repair service more versatile and efficient.
Data Source
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AI summary
The disclosure provides a method of an appliance, including receiving prediction information indicating a predicted failure of the appliance, obtaining a schedule for which use of a repair service for repairing the predicted failure based on the prediction information is available, transmitting a signal for requesting maintenance information used to delay the predicted failure and maintain a normal operation of the appliance if the obtained schedule is after a predicted failure time point indicated by the prediction information, receiving the maintenance information, and operating based on the maintenance information.