Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks

Author:

Zhang Lixia1,Gao Wei2,Yan Leifang1

Affiliation:

1. 1 Information and Communication Branch of State Grid of Shanxi Electric Power Company , Taiyuan , Shanxi , , China

2. 2 State Grid of Shanxi Electric Power Company , Taiyuan , Shanxi , , China .

Abstract

Abstract With the arrival of the big data era and the development of new network technology, how to use big data technology to diagnose and locate fiber optic cable faults in communication networks has become a hot topic of current concern. Firstly, a combined generative adversarial network and convolutional neural network algorithm is proposed based on a deep learning algorithm, then an improved fault diagnosis model combining generative adversarial network and convolutional neural network algorithm is built, and finally, the combined generative adversarial network and convolutional neural network model is used to verify and analyze the fiber optic cable fault diagnosis. The results show that the accuracy of the DCGAN-CNN algorithm for fiber optic cable fault diagnosis is 98.5%, and the research results verify the effectiveness of the combined generative adversarial network and convolutional neural network model for fiber optic cable fault diagnosis. This study can accurately and comprehensively solve the problem of fiber optic cable faults in communication networks and thus play a guiding reference value for developing fault diagnosis in Chinese communication networks.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

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