Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks

03/19/2022

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We propose a deep learning approach based on an autoencoder for identifying and localizing fiber faults in passive optical networks. The experimental results show that the proposed method detects faults with 97 pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.

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Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks

ML-based Anomaly Detection in Optical Fiber Monitoring

Machine Learning-based Anomaly Detection in Optical Fiber Monitoring

Branch Identification in Passive Optical Networks using Machine Learning

Fault Monitoring in Passive Optical Networks using Machine Learning Techniques

Faulty Branch Identification in Passive Optical Networks using Machine Learning

Simulation-to-reality UAV Fault Diagnosis with Deep Learning

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks

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