Open AccessFailure prediction using machine learning and time series in optical network
Author(s) -
Zhilong Wang,
Min Zhang,
Danshi Wang,
Chaosheng Song,
Min Liu,
Jin Li,
Liqi Lou,
Zhuo Liu
Publication year - 2017
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.25.018553
Subject(s) - computer science , exponential smoothing , support vector machine , machine learning , artificial intelligence , smoothing , focus (optics) , stability (learning theory) , time series , physics , optics , computer vision
In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.