Appliance Failure Prediction Using Selective Abnormal Data Transmission
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Solution Overview
Problem
Current home appliance systems lack efficient methods for predicting and managing failures, particularly in an IoT environment, where diverse user demands and advanced AI technologies require intelligent failure prediction and customized maintenance solutions.
Innovation Solution
A method and apparatus for selectively transmitting appliance operation data to a managing server, classifying it as normal or abnormal, and generating customized diagnosis treatment solutions based on user profiles, appliance settings, and historical data using AI-based data pattern detection routines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all appliance operation data is transmitted to the managing server for failure prediction, then the reliability of failure prediction is improved, but the loss of time and energy for data transmission increases
Solution Approach 1:
The patent applies local quality by classifying operation data into normal and abnormal categories, and selectively transmitting only abnormal data to the managing server. This selective transmission approach maintains high failure prediction reliability by ensuring critical abnormal data is captured, while significantly reducing data transmission time and energy consumption by filtering out normal operational data that does not require server processing.
2Manufacturing precision
If all appliance operation data is transmitted to the managing server for failure prediction, then the manufacturing precision of failure diagnosis is improved, but the use of energy for data transmission increases
Solution Approach 1:
The patent implements local quality through selective data transmission based on data type classification. By identifying and transmitting only abnormal operation data that requires attention, the system maintains high failure diagnosis precision while minimizing energy consumption associated with data transmission, as normal data is filtered out locally at the appliance level.
3Loss of time
If selective data transmission is implemented, then the loss of time for data transmission is reduced, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing operation data into distinct categories (normal and abnormal) based on predefined criteria. This segmentation enables efficient selective transmission where only abnormal data is sent to the server, reducing transmission time. The classification rules are designed to be straightforward, balancing the need for accurate data filtering with minimal system complexity.
Data Source
AI summary
A method performed by a managing server includes: receiving, from an electronic device, operation data of the electronic device; identifying, by using artificial intelligence (AI), a device usage pattern of the electronic device; identifying, by using the AI, information related to a failure or an abnormal operation of the electronic device and a solution to the failure or the abnormal operation based on the device usage pattern and the operation data received from the electronic device; and transmitting, to a user terminal, the information related to the failure or the abnormal operation of the electronic device and the solution to the failure or the abnormal operation.