Optical Amplifier Failure Prediction Using Machine Learning

Abstract

An optical network monitoring technique predicts degradation or failure of optical amplifiers using machine learning. Operational data associated with an optical amplifier, such as optical power measurements, pump parameters, spectral loading characteristics, and configuration parameters, is collected during in-service operation of the amplifier in an optical network. The operational data is analyzed using a trained machine learning model configured to learn relationships between operating conditions and normal amplifier behavior. Based on the analysis, the model generates prediction data indicative of a likelihood of degradation or impending failure of the optical amplifier. The prediction may include an anomaly score, classification, or other indicator of amplifier health. Responsive to the prediction, network management actions may be initiated, including generating alerts for proactive maintenance or reconfiguring traffic paths to avoid a degrading amplifier. The approach enables early identification of failing optical amplifiers using commonly available operational data without requiring specialized monitoring hardware.

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