Optical amplifier failure prediction using machine learning
AssigneeCiena Corporation
InventorsCarter Demars, Yinqing Pei, David W. Boertjes
Life of this patent
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Abstract
Systems and methods for optical amplifier failure prediction using Machine Learning (ML), such as for an Erbium-Doped Fiber Amplifier (EDFA), are described. A method include obtaining a plurality of inputs from an optical amplifier associated with an optical network; analyzing the plurality of inputs with a trained machine learning model; obtaining an estimate of a total pump current of the optical amplifier as an output of the trained machine learning model; and comparing the estimate of a total pump current to a measured total pump current of the optical amplifier. The steps can include determining a health of the optical amplifier based on the comparing.
Claims20
A non-transitory computer-readable medium having instructions stored thereon for programming a processor to perform steps of:
- obtaining a plurality of inputs from an optical amplifier associated with an optical network;
- analyzing the plurality of inputs with a trained machine learning model;
- obtaining an estimate of a total pump current of the optical amplifier as an output of the trained machine learning model;
- and
- comparing the estimate of a total pump current to a measured total pump current of the optical amplifier.
A method comprising steps of:
- obtaining a plurality of inputs from an optical amplifier associated with an optical network;
- analyzing the plurality of inputs with a trained machine learning model;
- obtaining an estimate of a total pump current of the optical amplifier as an output of the trained machine learning model;
- and
- comparing the estimate of a total pump current to a measured total pump current of the optical amplifier.
Dependencies are inferred from the claim text.