Failure Prediction in Optical Transport Networks Through the Integration of Digital Twins and Deep Learning

This work introduces an advanced comprehensive framework for predictive maintenance by integrating Digital Twin (DT) with multiple Deep Learning (DL) models with the aim of predicting amplifier failures in optical networks. Using GNPy (Gaussian Noise model in Python), an open source framework, a DT is created to emulate network behavior under both normal and failure conditions, enabling the generation of synthetic datasets representative of amplifier degradation fault scenarios. These datasets are used to train DL models based on Convolutional Neural Networks (CNN), Long Short- Term Memory (LSTM), and Long- and Short-Term Time-Series Networks (LSTNet). A comparative analysis shows that all models exhibit strong performance, with LSTM achieving an accuracy of 99% and LSTNet, CNN models closely following at 98% and 96% respectively, demonstrating the ability of these DL models to identify complex temporal and statistical patterns in network telemetry data, facilitating accurate prediction of early failures. This integrated solution provides a scalable and data-driven approach for proactive fault management, improving operational capabilities in optical transport networks.