Telecom Fault Prediction With Resource-Aware Action Selection
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Solution Overview
Problem
Existing fault management in telecommunications networks relies heavily on reactive approaches, which are inefficient and resource-intensive, and there is a need for proactive methods that balance prediction accuracy with resource usage to maintain high availability and reliability.
Innovation Solution
A method and node in a telecommunications network that uses machine learning models to predict faults and their time intervals, determines possible actions, and selects actions based on resource usage and time constraints to proactively manage faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proactive fault prediction approaches are implemented using machine learning models, then network reliability is improved by addressing faults before they occur, but resource usage increases due to the computational overhead and pre-emptive actions required
Solution Approach 1:
The patent changes the parameter of fault management timing from reactive to proactive by using machine learning models to predict faults before they occur. This allows the system to address faults at optimal times, improving reliability while managing resource usage through intelligent prediction rather than continuous monitoring or pre-emptive action on all potential faults.
Solution Approach 2:
The system uses machine learning models that automatically analyze network data and predict faults without requiring constant human intervention. The models serve themselves by continuously learning from historical data and improving prediction accuracy, reducing the resource burden on human operators while maintaining high reliability.
2Reliability
If pre-emptive actions are taken to address predicted faults, then network availability is improved, but resource consumption increases due to dispatching service personnel and performing maintenance actions
Solution Approach 1:
The patent applies preliminary action by predicting faults before they occur and scheduling maintenance actions in advance. This allows the system to address potential issues during off-peak times or before they impact service, improving availability while optimizing resource consumption by planning actions strategically rather than reacting to emergencies.
Solution Approach 2:
The system performs partial pre-emptive actions by only addressing predicted faults that meet certain confidence thresholds or priority levels. Not all predicted faults trigger pre-emptive maintenance actions, allowing the system to balance resource consumption with reliability improvements by focusing on the most critical predicted faults.
3Measurement precision
If machine learning models are used to predict faults with high accuracy, then prediction precision is improved, but device complexity increases due to the computational requirements and model management
Solution Approach 1:
The patent replaces traditional rule-based fault detection mechanisms with machine learning models that can identify complex patterns and predict faults with higher accuracy. This substitution allows the system to handle the complexity of modern networks automatically through intelligent algorithms rather than manual rule configuration, improving prediction precision while managing complexity through automation.
Data Source
AI summary
A method performed by a node in a telecommunications network for managing faults includes obtaining predictions of faults in the telecommunications network and time intervals in which the faults are predicted to occur. The method then includes determining possible actions that could be performed to address the predicted faults and associated resource usages to perform the possible actions, and selecting actions to perform, from the possible actions, in order to address the predicted faults, based on the predicted time intervals and the determined resource usages.