Laser degradation analysis is a crucial process for the enhancement of laser reliability. Here, we propose a data-driven fault detection approach based on Long Short-Term Memory (LSTM) recurrent neural networks to detect the different laser degradation modes based on synthetic historical failure data. In comparison to typical threshold-based systems, attaining 24.41% classification accuracy, the LSTM-based model achieves 95.52% accuracy, and also outperforms classical machine learning (ML) models namely Random Forest (RF), K-Nearest Neighbours (KNN) and Logistic Regression (LR).
@article{arxiv.2203.11729,
title = {Machine Learning based Laser Failure Mode Detection},
author = {Khouloud Abdelli and Danish Rafique and Stephan Pachnicke},
journal= {arXiv preprint arXiv:2203.11729},
year = {2022}
}
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21st International Conference on Transparent Optical Networks (ICTON) 2019