2025 Vol.8 Aug.N04

• Title: Fault diagnosis method for laser communication network based on artificial intelligence technology
• Name: Lucas Daniel Wilson
• Company: Thomas Jefferson University,USA
• Abstract:

  A laser communication network fault diagnosis method based on artificial intelligence technology is proposed to address the problem of bias in fault diagnosis results caused by a small number of fault data samples in laser communication network fault diagnosis. Collect the optical time domain reflectometer curve under the operating conditions of the laser communication network, and analyze the types of faults in the laser communication network based on this curve. Based on this, a laser communication network fault diagnosis model is constructed using the Generative Adversarial Network (GAN) and XGBoost algorithm in artificial intelligence technology; Expand the training dataset using GAN, output simulated communication fault data samples under given noise conditions through the generator inside GAN, label the simulated communication fault data samples and initial communication fault data samples with the recognizer inside GAN, and improve the quality of laser communication fault data processing through the competitive relationship between the generator and recognizer; Based on the expanded training dataset, an XGBoost fault diagnosis model is constructed. The XGBoost model is trained using labeled fault data samples to learn the characteristics and classification rules of the fault data. The laser communication network data to be diagnosed is input into the trained XGBoost model, which outputs information such as fault type and severity. The experimental results show that this method can accurately diagnose various types and causes of faults in laser communication networks, reduce network failure time, and ensure the safety of laser communication network operation.

  
  

• Keyword: Artificial intelligence technology; Laser communication network; Fault diagnosis; Optical Time Domain Reflectometer; Generate adversarial networks; XGBoost algorithm
• DOI: 10.12250/jpciams2025090801
• Citation form: Lucas Daniel Wilson.Fault diagnosis method for laser communication network based on artificial intelligence technology[J]. Computer Informatization and Mechanical System,2025,Vol.8,pp.1-6

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