Telecom Mass Outage Detection via Alarm Correlation
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Solution Overview
Problem
Existing telecommunication networks rely on manual intervention to identify mass outages, which reduces response time and increases manpower requirements, leading to decreased customer satisfaction due to service disruptions.
Innovation Solution
A system that generates relationship rules between equipment to automatically identify mass outages and generate incident reports, allowing for automated detection and prioritization of equipment issues based on geographic location, equipment type, and problem classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to identify mass outages, then the system complexity is reduced, but the response time increases and productivity decreases
Solution Approach 1:
The system enables self-service by allowing equipment to automatically generate and transmit alarm data without human intervention. The automated correlation engine processes alarm data, applies correlation rules, and identifies mass outages independently, eliminating the need for manual monitoring and analysis while reducing response time.
Solution Approach 2:
The patent replaces manual mechanical processes with automated electronic systems. The correlation engine uses computer processing to substitute manual analysis of alarm data, applying electronic correlation rules instead of human judgment to identify mass outages, thereby increasing productivity and reducing response time.
2Productivity
If alarms are addressed one at a time based on reception order, then the process simplicity is maintained, but the loss of time increases and productivity decreases
Solution Approach 1:
The system merges multiple individual alarm processing operations into a single automated correlation process. The correlation engine aggregates alarm data from multiple equipment components, applies correlation rules to identify patterns, and determines mass outages simultaneously, replacing sequential one-at-a-time processing with parallel batch processing.
Solution Approach 2:
The system performs preliminary actions by pre-establishing correlation rules that define relationships between equipment components and alarm patterns. These pre-configured rules enable the correlation engine to quickly identify mass outages without requiring real-time manual analysis, reducing the time needed to address multiple alarms.
3Productivity
If automated correlation rules are implemented, then the productivity increases and response time decreases, but the device complexity increases
Solution Approach 1:
The system segments the complex correlation functionality into discrete, manageable correlation rules that can be independently configured and applied. Each rule handles specific alarm patterns or equipment relationships, allowing the system to process complex alarm data through multiple simple, modular rules rather than a single complex system.
Solution Approach 2:
The patent utilizes parameter changes by allowing dynamic adjustment of correlation thresholds and rule parameters. The system can modify correlation criteria based on network conditions, equipment types, and alarm patterns, enabling flexible adaptation to different scenarios without requiring complex reconfiguration of the entire correlation system.
Data Source
AI summary
A system includes a non-transitory computer readable medium configured to store instructions thereon; and a processor. The processor is configured to receive alarm data related to equipment in a telecommunication network. The processor is configured to receive a correlation rule and a mass outage rule, wherein each mass outage rule is associated with a correlation rule. The processor is configured to perform a first aggregation using the alarm data based on domain, vendor, equipment type, classification, or geographic information; and associate the aggregated alarm data with a corresponding correlation rule. The processor is configured to determine a number of pieces of equipment that are associated with each correlation rule; determine whether the determined number is equal to or greater than a threshold value; and generate an incident report in response to the number of pieces of equipment being equal to or greater than the at least one threshold value.