Appliance Failure Prediction Using Selective Abnormal Data Transmission
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Solution Overview
Problem
Current home appliance failure prediction systems lack efficiency in data transmission and personalized diagnosis, leading to suboptimal maintenance and repair services.
Innovation Solution
A method and apparatus for selectively transmitting appliance operation data to a managing server for failure prediction, which classifies data into normal and abnormal categories, and generates customized diagnosis treatment solutions based on user profiles, appliance settings, and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all appliance operation data is transmitted to the server for failure prediction, then the accuracy of failure prediction is improved, but the data transmission load and processing time increase
Solution Approach 1:
The patent extracts and transmits only abnormal operation data that deviates from normal patterns, rather than transmitting all operation data. The determination unit identifies abnormal data by comparing operation data against stored normal patterns, and only transmits data that does not match these patterns. This extraction principle reduces data transmission volume while maintaining failure prediction accuracy by focusing on diagnostically relevant information.
Solution Approach 2:
The patent segments operation data into normal and abnormal categories based on pattern matching. By dividing the data stream into distinct types and selectively transmitting only the abnormal segment, the system reduces overall transmission load. The server receives segmented data that is more efficiently processed for failure prediction, separating routine operational data from potentially problematic data points.
2Measurement precision
If detailed operation data is transmitted for accurate failure prediction, then the diagnosis precision is improved, but the data transmission quantity increases
Solution Approach 1:
The determination unit extracts only the essential abnormal data characteristics that are relevant for failure diagnosis. Instead of transmitting complete detailed operation data, the system extracts specific data points that deviate from normal patterns and transmits only these extracted abnormalities to the server. This maintains diagnosis precision by focusing on the most informative data while significantly reducing transmission quantity.
Solution Approach 2:
The patent applies local quality by enhancing the detail and precision of abnormal data transmission while reducing or omitting normal data. The abnormal data is transmitted with sufficient detail for accurate diagnosis, while normal operational data is either omitted or transmitted in reduced form. This selective quality allocation optimizes the balance between diagnosis precision and data transmission quantity.
3Device complexity
If generic failure prediction services are provided, then the system complexity is reduced, but the adaptability to different users and appliances decreases
Solution Approach 1:
The system performs preliminary actions by pre-storing normal operation data patterns in the determination unit before actual failure prediction occurs. These pre-established patterns serve as baseline references for identifying abnormalities. By preparing these reference patterns in advance, the system enables rapid, adaptive failure detection without requiring complex real-time analysis, thus maintaining low system complexity while achieving personalized adaptation to specific appliance operations.
Solution Approach 2:
The system implements feedback mechanisms where the server provides failure prediction results and diagnosis information back to the determination unit. This feedback loop enables the system to learn from predicted failures and refine its abnormal data identification criteria. The feedback principle allows the system to adapt to different users and appliances dynamically without increasing structural complexity, as the adaptation occurs through iterative learning from feedback rather than through complex pre-programming.
Data Source
AI summary
A method performed by an appliance includes receiving, from a managing server, information about a data pattern detection routine to detect abnormal data among operation data of the appliance, determining whether the operation data of the appliance matches a normal data pattern defined by the data pattern detection routine, determining the operation data as the abnormal data when the operation data does not match the normal data pattern, and transmitting the abnormal data to the managing server.