Machine Learning Agents Leveraging Digital Twins for Failure Prediction in Optical Networks

This study proposes an advanced framework for predicting optical amplifier failures in optical networks by integrating Digital Twins (DT) and Machine Learning (ML). Utilizing the GNPy open-source framework, DTs replicate amplifier behavior under various conditions, resembling faults and captures the network conditions and performance metrics of the optical networks. The telemetry data generated from these simulations represents both short-term dynamics and long-term trends in amplifier performance, enabling the training of a Long Short-Term Memory (LSTM) model. The ML model demonstrates an amplifier fault level classification accuracy of 98%, effectively identifying soft failures and assessing fault severity. By leveraging the ability to model complex fault scenarios in a controlled environment, the framework provides a comprehensive solution for generating datasets that are otherwise difficult to obtain from live networks. This approach enables early detection and intervention, minimizes service disruptions, and enhances network reliability. The integration of DTs and LSTM-based ML offers a scalable and data-driven solution for improving the resilience, efficiency, and operational continuity of modern optical communication systems.