Open Access

ARTICLE

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

Xiangqun Li^1,*, 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:

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

Received 11 February 2023; Accepted 06 April 2023; Issue published 27 December 2023

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.

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