Paper
8 April 2024 Research on intelligent fault detection method for OTN based on BERT neural network
Mingming Zhao, Zhenwei Wu, Jun Yu, Wang Li, Yunfei Xiong, Guo Wan, Weifu Fu, Qiang Dong
Author Affiliations +
Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901G (2024) https://doi.org/10.1117/12.3025967
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China
Abstract
With the continuous maturation of data mining technologies, intelligent fault detection in optical networks has become a prominent area of research in optical network fault analysis. However, due to the complexity of domestic optical networks, intelligent fault detection in optical networks still faces significant challenges. This paper investigates an intelligent fault detection method for Optical Transport Network (OTN) based on the BERT neural network. The research begins with the analysis of raw alarm data obtained from collection tools. The feature data was extracted from the alarm information. Subsequently, employing clustering analysis methods, the paper conducts a clustering analysis of OTN network fault alarm data. The parameters such as alarm time, duration, business network, and element network were considered to categorize different fault work order information. Finally, the fault category information was used as labels for OTN network fault alarm data, and the improved BERT neural network was trained. After obtaining a well-trained BERT model, it is deployed in the live network environment to achieve intelligent detection of OTN network faults.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Mingming Zhao, Zhenwei Wu, Jun Yu, Wang Li, Yunfei Xiong, Guo Wan, Weifu Fu, and Qiang Dong "Research on intelligent fault detection method for OTN based on BERT neural network", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901G (8 April 2024); https://doi.org/10.1117/12.3025967
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KEYWORDS
Neural networks

Education and training

Analytical research

Optical networks

Data modeling

Feature extraction

Data mining

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