Fault Prediction Logic Generation for Client Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information processing systems for predicting faults in devices like copiers and printers often rely on external cloud services for fault prediction logic, which may not be effective in client-side environments, lacking real-time monitoring and action capabilities.
Innovation Solution
An information processing system that analyzes history information from client-side devices to generate fault prediction logic, which is then built into both the cloud service system and client-side apparatuses, enabling real-time monitoring and predetermined actions when fault conditions are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external cloud services are used for fault prediction logic, then centralized data analysis capability is improved, but real-time monitoring and response capability deteriorates
Solution Approach 1:
The fault prediction system is segmented into two parts: a cloud-based analysis server that performs comprehensive historical data analysis to generate prediction logic, and local prediction units embedded in client devices that execute the generated logic in real-time. This segmentation allows centralized intelligence to be combined with distributed real-time execution, resolving the contradiction between accurate prediction and rapid response.
2Extent of automation
If fault prediction logic is built into client-side apparatuses, then real-time monitoring capability is improved, but system complexity increases
Solution Approach 1:
The complex task of fault prediction logic generation is performed in advance by the cloud-based analysis server using comprehensive historical data and advanced algorithms. The resulting prediction logic is then simplified and embedded in client devices. This preliminary action transfers the computational complexity to the cloud while keeping client-side implementation simple, enabling automated real-time monitoring without significantly increasing client device complexity.
3Measurement precision
If comprehensive history information is analyzed centrally, then prediction accuracy is improved, but data transmission requirements increase
Solution Approach 1:
The system extracts and transmits only the essential prediction logic and relevant monitoring parameters from the comprehensive historical analysis performed centrally, rather than transmitting all raw historical data to client devices. This extraction approach maintains prediction accuracy by preserving the core predictive patterns while significantly reducing the volume of data that needs to be transmitted and stored locally.
Data Source
AI summary
An information processing system includes a first and second information processing systems respectively including first and second apparatuses, and a third information processing system storing history information of the first apparatus and implementing analyzing the history information to acquire a degree of influence for an item included in the history information corresponding to a fault in the first apparatus, and generating a fault prediction logic of predicting a fault in the first apparatus using the item having a relatively high degree of influence and a value of a case where the fault occurs in the first apparatus based on a result of the analysis, and building the generated fault prediction logic into the first and second apparatuses, the first and second information processing system monitoring the history information, and detecting a state of matching a content of the built-in fault prediction logic.