Demonstration of Real-Time AI-Enabled Smart Fault Detection using State-of-Polarization Monitoring

In this demo, we present a real-time, machine-learning-driven framework for early fault detection in optical fiber networks, leveraging continuous State-of-Polarization (SOP) monitoring and angular speed (SOPAS) analysis. By extracting polarization fingerprints from a Polarimeter and feeding them into a trained ML classifier, our system detects and categorizes physical anomalies, such as small hits, slow shake (oscillations), and fast shake (oscillations) on the fiber, before they escalate into service disruptions. This proactive mechanism enables timely alerts and a direction towards dynamic traffic rerouting, preserving network integrity. The demonstration showcases a fully functional remote pipeline that integrates AI-based sensing, classification, and automated response, laying the foundation for self-monitoring optical infrastructures