Digital Twin-Integrated Binary Classifier ML Model for EDFA Failure Prediction

This study presents a digital twin-enabled framework integrated with a binary classification Machine Learning (ML) model for forecasting failures in Erbium-Doped Fiber Amplifiers (EDFAs). The framework utilizes GNPy, an opensource optical network planning tool, to construct a digital twin that serves as a virtual replica of the physical EDFA system. This digital twin facilitates the estimation of Quality of Transmission (QoT), along with the collection and analysis of key operational parameters. A binary classification model, based on Long Short-Term Memory (LSTM) networks, is trained on the data generated by the digital twin to predict potential EDFA failures, achieving a high prediction accuracy of 98 %. This predictive capability enables early fault detection and proactive maintenance, thereby minimizing unplanned downtime and service disruptions. By incorporating real-time analytics and predictive insights, the proposed approach significantly enhances the reliability, availability, and intelligence of optical network management.