Failure prediction using machine learning and time series in optical network
Open accessAbstract
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.
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Wang et al. [469] enhanced SVM accomplishes an accuracy of 95% in predicting equipment failure based on real data from an optical network operator.
“The enhanced SVM accomplish an accuracy of 95% in predicting equipment failure based on real data from an optical network operator.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [469]
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Self-citation
- Authors
- Wang, Z., Zhang, M., Wang, D., Song, C., Liu, M., Li, J., Lou, L., & Liu, Z.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- China Mobile (China) (CN)
- Venue
- Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2017-07-24
- Volume
- 25
- Issue
- 16
- Pages
- 18553
- ISSN
- 1094-4087 (ISSN-L)
- OpenAlex
- W2738710562
- Cited by
- 191
- References
- 11
- Citations by year
- 2026: 9 · 2025: 21 · 2024: 15 · 2023: 25 · 2022: 26 · 2021: 23 · 2020: 26 · 2019: 32 · 2018: 14
- FWCI
- 8.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Optics Express (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (NSFC) (61372119)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Network Technologies
- Keywords
- equipment failure risk, support vector machine, double exponential smoothing, performance monitoring, time series forecasting, network reliability
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