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Failure prediction using machine learning and time series in optical network.

Zhilong Wang, Min Zhang, Danshi Wang

|October 19, 2017

Summary

This study introduces a machine learning method using support vector machine (SVM) and double exponential smoothing (DES) to predict optical equipment failures. The novel approach achieves 95% accuracy, enhancing optical network stability and service protection.

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Area of Science:

Telecommunications Engineering
Network Reliability
Machine Learning Applications

Background:

Optical networks are critical infrastructure requiring robust performance monitoring.
Predicting equipment failures is essential for maintaining network stability and preventing service disruptions.
Existing risk-aware models lack comprehensive equipment failure prediction capabilities.

Purpose of the Study:

To propose a novel machine learning-based method for performance monitoring and failure prediction in optical networks.
To investigate the prediction of equipment failure risk in optical networks.
To enhance traditional risk-aware models with advanced prediction techniques.

Main Methods:

Implementation of a hybrid machine learning model combining Support Vector Machine (SVM) and Double Exponential Smoothing (DES).
Focus on developing risk-aware models for optical network equipment.
Utilizing historical performance data for failure state prediction.

Main Results:

The proposed DES-SVM method achieved an average prediction accuracy of 95% for optical equipment failure states.
Demonstrated high accuracy in forecasting equipment failure risks.
The method effectively identifies potential equipment failures before they occur.

Conclusions:

The developed DES-SVM method significantly improves upon traditional risk-aware models in optical networks.
Accurate failure prediction enhances service protection and overall optical network stability.
This approach offers a valuable tool for proactive network maintenance and management.