Abstract

The article addresses the problem of low responsiveness of emergency restoration operations on fiber-optic communication lines (FOCL) in remote and hard-to-reach areas. The slowdown is caused by the combined effect of geographical, logistical, and staffing constraints, which leads to an increase in mean time to repair (MTTR). The aim of the study is to theoretically substantiate the effectiveness and practical feasibility of predictive diagnostics based on machine learning algorithms for analyzing data from optical time-domain reflectometers (OTDR). The methodological basis includes a systematic review and integrative synthesis of recent academic publications and industry reports in recent years. As a result, a conceptual system architecture with four levels is presented: data collection, data processing, application of machine learning methods, and generation of actionable recommendations. A comparative analysis of machine learning models (CNN, LSTM, ensemble methods) for interpreting reflectograms is conducted; the strengths and weaknesses of these approaches are identified with respect to event classification and optical fiber degradation forecasting tasks. It is concluded that the implementation of such a system shifts the maintenance of ВОЛС from a reactive to a proactive paradigm, ensuring a substantial reduction in MTTR (up to 39%), a decrease in operational costs (up to 40%), and increased reliability of the network infrastructure. The study is addressed to communications engineers, telecommunications company managers, as well as data analytics and artificial intelligence specialists working to enhance the resilience of critical infrastructure.

Keywords

  • fiber-optic communication lines
  • predictive diagnostics
  • optical time-domain reflectometer
  • machine learning
  • remote regions
  • mean time to repair
  • network reliability
  • operational costs
  • convolutional neural networks
  • recurrent neural networks

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