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Fault Monitoring in Passive Optical Networks using Machine Learning Techniques
Passive optical network (PON) systems are vulnerable to a variety of failures, including fiber cuts and optical network unit (ONU) transmitter/receiver failures.
Faulty Branch Identification in Passive Optical Networks using Machine Learning
In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths.
Branch Identification in Passive Optical Networks using Machine Learning
A machine learning approach for improving monitoring in passive optical networks with almost equidistant branches is proposed and experimentally validated.
Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder
Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks.
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication
First of all, an attention based gated recurrent unit (GRU) model is adopted for real-time prediction of performance degradation.
Machine Learning based Laser Failure Mode Detection
Laser degradation analysis is a crucial process for the enhancement of laser reliability.
Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory
To reduce operation-and-maintenance expenses (OPEX) and to ensure optical network survivability, optical network operators need to detect and diagnose faults in a timely manner and with high accuracy.
Machine Learning based Data Driven Diagnostic and Prognostic Approach for Laser Reliability Enhancement
In this paper, a data-driven diagnostic and prognostic approach based on machine learning is proposed to detect laser failure modes and to predict the remaining useful life (RUL) of a laser during its operation.
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring
The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm, whereby the former is used for fault detection and the latter is adopted for fault diagnosis and localization once an anomaly is detected by the autoencoder.
A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction
A hybrid prognostic model based on convolutional neural networks (CNN) and long short-term memory (LSTM) is proposed to predict the laser remaining useful life (RUL).
Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers
A new privacy-preserving federated learning framework allowing laser manufacturers to collaboratively build a robust ML-based laser lifetime prediction model, is proposed.
Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques
A novel approach based on an artificial neural network (ANN) for lifetime prediction of 1. 55 um InGaAsP MQW-DFB laser diodes is presented.
Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals
Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability.
Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks
We propose a deep learning approach based on an autoencoder for identifying and localizing fiber faults in passive optical networks.
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults.
ML-based Anomaly Detection in Optical Fiber Monitoring
Secure and reliable data communication in optical networks is critical for high-speed internet.
A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis
A novel multitask learning approach based on stacked bidirectional long short-term memory (BiLSTM) networks and convolutional neural networks (CNN) for detecting, locating, characterizing, and identifying fiber faults is proposed.
Predictive Maintenance for Optical Networks in Robust Collaborative Learning
Federated learning (FL) is a promising candidate to tackle the aforementioned challenge by enabling the development of a global ML model using datasets owned by many vendors without revealing their business-confidential data.
