Machine Learning based Laser Failure Mode Detection
This addresses laser reliability for industrial or maintenance applications, but it is incremental as it applies an existing method (LSTM) to a specific domain.
The paper tackled laser degradation analysis by proposing an LSTM-based fault detection approach, achieving 95.52% classification accuracy compared to 24.41% for threshold-based systems and outperforming classical ML models.
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).