Machine Learning based Laser Failure Mode Detection

• URL: http://arxiv.org/abs/2203.11729v1
• Date: Sat, 19 Mar 2022 09:46:19 GMT
• Title: Machine Learning based Laser Failure Mode Detection
• Authors: khouloud Abdelli, Danish Rafique, and Stephan Pachnicke
• Abstract summary: 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. attaining 24.41% classification accuracy, the LSTM-based model achieves 95.52% accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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