Open Access iconOpen Access

ARTICLE

Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering

Xiangqun Li1,*, Jiawen Liang2, Jinyu Zhu2, Shengping Shi2, Fangyu Ding2, Jianpeng Sun2, Bo Liu2

1 Gannan Power Supply Company of State Grid Gansu Electric Power Supply Company, Hezuo, 747000, China
2 Digital Communication Department, Gannan Power Supply Company of State Grid Gansu Electric Power Supply Company, Hezuo, 747000, China

* Corresponding Author: Xiangqun Li. Email: email

Energy Engineering 2024, 121(1), 203-219. https://doi.org/10.32604/ee.2023.029295

Abstract

To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis, this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition (VMD), fuzzy entropy (FE) and fuzzy clustering (FC). Firstly, based on the OTDR curve data collected in the field, VMD is used to extract the different modal components (IMF) of the original signal and calculate the fuzzy entropy (FE) values of different components to characterize the subtle differences between them. The fuzzy entropy of each curve is used as the feature vector, which in turn constructs the communication optical fibre feature vector matrix, and the fuzzy clustering algorithm is used to achieve fault diagnosis of faulty optical fibre. The VMD-FE combination can extract subtle differences in features, and the fuzzy clustering algorithm does not require sample training. The experimental results show that the model in this paper has high accuracy and is relevant to the maintenance of communication optical fibre when compared with existing feature extraction models and traditional machine learning models.

Keywords


Cite This Article

Li, X., Liang, J., Zhu, J., Shi, S., Ding, F. et al. (2024). Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering. Energy Engineering, 121(1), 203–219.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 292

    View

  • 169

    Download

  • 0

    Like

Share Link