  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).
