Networked Alarm Prediction Using a Machine Learning Classifier
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Solution Overview
Problem
Managing alarm traffic in communication networks is challenging due to the large quantity of alarms and the difficulty in interpreting them in a timely manner to minimize the impact on network functioning, particularly in large network environments.
Innovation Solution
A machine learning solution is employed to predict the occurrence of future alarms in a network environment using a machine learning classifier trained with historical maintenance records, allowing for the prediction of alarm sequences and necessary responses, such as work orders, before the alarms occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alarm management systems are used to monitor network faults, then network reliability can be maintained through fault detection, but the large quantity of alarms generated by single faults makes timely interpretation and response difficult
Solution Approach 1:
The system performs preliminary action by predicting future alarms before they actually occur. The machine learning model analyzes historical alarm data and network state to forecast potential faults, allowing proactive response before the alarms manifest, thus reducing response time while maintaining reliability
Solution Approach 2:
The system implements feedback by continuously monitoring actual alarm occurrences and comparing them with predictions. This feedback loop allows the machine learning model to refine its accuracy over time, improving prediction reliability and reducing false alarms, thereby enabling faster and more accurate responses
2Loss of time
If machine learning prediction is implemented to forecast alarms, then proactive response and reduced fault impact time are achieved, but the complexity of the system increases due to training requirements and data processing
Solution Approach 1:
The system applies self-service by automatically training and optimizing the machine learning model using historical alarm data without requiring manual intervention. The model self-adjusts to improving accuracy over time, reducing the operational complexity of managing the prediction system while maintaining reduced fault impact time
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting model parameters and thresholds based on learned patterns from historical data. This allows the system to adapt to changing network conditions and optimize prediction accuracy without requiring manual reconfiguration, thereby managing complexity while achieving timely responses
Data Source
AI summary
According to an example aspect of the present invention, there is provided an apparatus configured to store a set of parameters of a machine learning classifier configured to predict networked alarms, the set of parameters comprising at least one maximum time interval, process a first alarm signal sequence originating in a networked environment, consecutive alarms comprised in the first alarm signal sequence occurring at most a time interval comprised in the at least one maximum time interval from each other, and predict, using the set of parameters of the machine learning classifier and the machine learning classifier, based on the first alarm signal sequence, at least one second alarm signal to occur during a first time interval.