Pinpointing Faulty Optical Amplifiers: Unsupervised Learning on Real Telemetry Data

We propose an unsupervised machine-learning (ML) method to localize faulty optical amplifiers within large optical networks using power time-series data. An amplifier is considered faulty if an anomaly is detected at its output power but not at its input power, indicating that the anomaly is generated in the monitored device. Time-series anomalies are detected using an ML-based prediction engine that has been adapted to replace a proprietary core with widely adopted algorithms. The results indicate effective anomaly detection performance and demonstrate the successful localization of a faulty amplifier within a real network.