ML Fault Escalation Engine for Persistent Network Alarms
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Solution Overview
Problem
Conventional fault management systems in large networks struggle to accurately escalate alarms due to hard-coded rules, often missing critical faults while escalating non-essential ones, especially in networks with numerous devices.
Innovation Solution
Implementing a fault escalation engine that uses machine learning techniques, such as Support Vector Machines (SVM) and statistical analysis (e.g., Confidence Intervals and Analysis of Variance) to intelligently categorize alarms based on persistence, repetition, and recovery, allowing for more accurate escalation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If hard-coded rules are used to automate fault management, then automation extent is improved, but measurement precision of fault detection deteriorates
Solution Approach 1:
The patent replaces hard-coded mechanical rule-based systems with machine learning models that can dynamically learn and adapt to fault patterns. The SVM classifier and statistical analysis mechanisms substitute the rigid if-then rule structure with adaptive computational models that improve detection accuracy while maintaining automation.
Solution Approach 2:
The system changes the parameters of fault detection by moving from fixed threshold values in hard-coded rules to dynamic parameters derived from statistical analysis and machine learning. The escalation thresholds are no longer static but are determined through confidence intervals and SVM decision boundaries that adapt to actual fault patterns.
2Device complexity
If hard-coded escalation rules are applied, then device complexity is reduced, but reliability of fault management deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors alarm patterns and uses statistical analysis to refine escalation decisions. The machine learning models learn from historical alarm data and improve their predictions over time, creating a closed-loop system that enhances reliability while managing complexity through automated adaptation.
Solution Approach 2:
The system performs preliminary analysis of alarm patterns using SVM and statistical methods before making escalation decisions. By pre-processing alarm data and identifying patterns in advance, the system builds a knowledge base that improves the reliability of subsequent escalation decisions without requiring complex real-time analysis.
3Ease of operation
If conventional rule-based systems are used, then ease of operation is improved, but loss of information about fault patterns increases
Solution Approach 1:
The patent introduces machine learning models and statistical analysis as intermediaries between raw alarm data and escalation decisions. These intermediary systems process and interpret complex fault patterns, extracting meaningful information that would be lost in simple rule-based systems while maintaining ease of operation through automated analysis.
Solution Approach 2:
The system combines multiple analytical approaches (SVM, confidence intervals, ANOVA) into a composite intelligence framework. This composite approach integrates different types of information processing to create a more comprehensive understanding of fault patterns than any single method could provide alone.
Data Source
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AI summary
A processing system includes a memory storing a program of instructions, and a processor coupled to the memory and configured to execute the program of instructions. The processor monitors a network, which includes a plurality of devices, for alarms generated by the plurality of devices. The alarms are associated with alarm conditions, and the processor assigns the alarm conditions associated with received alarms to escalation-type categories based on Support Vector Machine learning analysis of alarm characteristics of the alarm conditions. The processor also determines whether a first alarm condition associated with a first received alarm satisfies a first escalation criterion associated with a first escalation-type category to which the first alarm condition has been assigned, and triggers an escalation action in response to determining that the first alarm condition satisfies the first escalation criterion.