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

• Published in: Applied mathematics and nonlinear sciences 2024-01, Vol.9 (1)
• Main Authors: Zhang, Lixia, Gao, Wei, Yan, Leifang
• Format: Article
• Language: English
• Subjects: 78-02; Adversarial network; Communication network fiber optic cable fault; Convolutional neural network; DCGAN-CNN algorithm; Deep learning algorithm
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns.2023.2.00241

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.