Temporal Graph Device Failure Prediction
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Solution Overview
Problem
Current methods for predicting device failures in complex systems are inefficient, relying on costly and time-consuming hardware investigations, and often fail to accurately anticipate failures within a predetermined time frame, leading to delayed decision-making and scalability issues.
Innovation Solution
The implementation of a system and method using temporal graphs to represent device failure data, extracting vector representations that capture temporal and structural correlations, and predicting device failures based on these metrics, thereby reducing time delays and improving scalability in decision-making processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware investigations are used to predict device failures, then prediction accuracy may be improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces physical hardware investigations with a computational model based on temporal graphs and deep learning. The system uses communication data and device metrics to build temporal graphs that capture device behavior patterns, then applies neural networks to predict failures without physical inspection, thereby eliminating time-consuming hardware investigations while maintaining prediction accuracy
Solution Approach 2:
The patent creates a virtual representation (temporal graph) that copies and simulates device behavior patterns from communication data. This virtual model allows prediction of device failures by analyzing patterns in the copied data rather than performing actual hardware investigations, significantly reducing time consumption while preserving predictive capability
2Measurement precision
If hardware investigations are performed for each device, then individual prediction accuracy improves, but scalability deteriorates
Solution Approach 1:
The patent creates a universal prediction system that processes multiple devices simultaneously using the same temporal graph framework. The system handles diverse device types and communication protocols through a unified architecture, enabling scalable deployment across large device fleets while maintaining individual prediction accuracy through device-specific temporal graph analysis
Solution Approach 2:
The patent merges the processing of multiple devices into a single unified system that handles all devices through common infrastructure. By combining data collection, temporal graph construction, and prediction algorithms into an integrated platform, the system achieves economies of scale that improve both productivity and scalability while preserving individual device analysis capability
3Ease of operation
If traditional prediction methods are used, then implementation simplicity is maintained, but decision-making speed decreases
Solution Approach 1:
The patent performs preliminary analysis by continuously building and updating temporal graphs from communication data in real-time. This preliminary action captures device behavior patterns and anomalies before failures occur, enabling rapid prediction and decision-making when needed without complex real-time investigation, thus improving speed while maintaining operational simplicity
Solution Approach 2:
The system enables self-service prediction by automatically collecting communication data, constructing temporal graphs, and generating failure predictions without manual intervention. This automation accelerates decision-making speed while keeping the system easy to operate, as the predictive functionality serves itself through automated data processing and analysis
Data Source
AI summary
Systems and methods for predicting system device failure are provided. The method includes representing device failure related data associated with the devices from a predetermined domain by temporal graphs for each of the devices. The method also includes extracting vector representations based on temporal graph features from the temporal graphs that capture both temporal and structural correlation in the device failure related data. The method further includes predicting, based on the vector representations and device failure related metrics in the predetermined domain, one or more of the devices that is expected to fail within a predetermined time.