Machine Learning for Predictive Multi-Event Detection in Fiber Optic Systems

We apply Machine Learning (ML) to detect and classify anomalies in optical fiber systems. Fiber optic systems are highly sensitive to external factors such as mechanical stress, vibrations, and malicious manipulations, leading to potential service disruptions and data loss. Using State of Polarization (SOP) data and its angular speed (SOPAS), we developed a ML framework capable of detecting multi-event anomalies, including small hits, up and down fiber movement, and shaking. Experimental data from polarization signature monitoring were classified using supervised ML models, namely Random Forest, Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting (XGBoost). A Weighted Performance Metric (WPM) was employed to evaluate model performance by balancing accuracy and training time. The results demonstrate the superior effectiveness of Random Forest and XGBoost in achieving reliable anomaly detection while maintaining computational efficiency. This study underscores the transformative role of ML in predictive anomaly detection, enabling proactive maintenance and enhancing the reliability of optical communication systems.