Wireless Telecommunication Alarm Correlation for Root Cause Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Diagnosing the root cause of issues in wireless telecommunication networks is challenging due to alarms propagating through interconnected components, leading to prolonged resolution times and inefficient problem-solving processes.
Innovation Solution
A system that creates correlation and causation signatures from multiple alarms, using historical data and machine learning to predict the root cause, and generates automated tickets for resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional alarm analysis methods are used in wireless telecommunication networks, then all alarms are processed individually, but this leads to prolonged resolution times and many unnecessary attempts
Solution Approach 1:
The patent segments the complex alarm analysis problem by creating distinct signature components: correlation signatures that group related alarms together, causation signatures that identify root causes, and effect signatures that track propagated impacts. This segmentation transforms the overwhelming task of analyzing all alarms individually into manageable signature-based patterns, directly reducing resolution time while improving productivity.
Solution Approach 2:
The patent introduces signature-based patterns as intermediary representations between raw alarms and root cause identification. These signatures act as mediators that encode relationships between alarms, allowing the system to infer causation without directly analyzing every alarm interaction. This intermediary layer dramatically reduces analysis time while maintaining high accuracy in identifying root causes.
2Reliability
If alarms are propagated through interconnected network components, then comprehensive monitoring is achieved, but the root cause becomes difficult to diagnose among many raised alarms
Solution Approach 1:
The patent extracts the essential diagnostic information from the complex web of propagated alarms by creating correlation signatures that isolate the unique pattern of alarm relationships. Instead of analyzing all alarms equally, the system extracts and focuses on the signature pattern that identifies the root cause, separating the signal from the noise of propagated effects. This extraction process maintains comprehensive monitoring coverage while making root cause detection straightforward.
Solution Approach 2:
The patent inverts the traditional approach by not starting with individual alarms and trying to find the root cause, but rather by establishing signature patterns of root causes first and then matching observed alarm patterns against these signatures. This inversion transforms the difficult problem of finding needles in haystacks into the easier problem of pattern matching, maintaining full monitoring coverage while simplifying root cause identification.
Data Source
AI summary
The system receives multiple alarms indicating the issue associated with the network and obtains multiple categories associated with the multiple alarms. The category indicates a component associated with the network. Based on the multiple categories, the system creates a correlation signature associated with the multiple alarms. The system obtains historical data including a historical correlation signature that is the same as the correlation signature, a cause associated with the historical correlation signature, and an indication of accuracy associated with the cause. The system determines whether the indication of accuracy satisfies a first criterion. Upon determining that the indication of accuracy satisfies the first criterion, the system makes a prediction that the cause associated with the multiple alarms indicating the issue is the same as the cause associated with the historical correlation signature.