DeepAI AI Chat
Log In Sign Up

Machine Learning based Laser Failure Mode Detection

by   Khouloud Abdelli, et al.

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 accuracy, the LSTM-based model achieves 95.52 classical machine learning (ML) models namely Random Forest (RF), K-Nearest Neighbours (KNN) and Logistic Regression (LR).


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...

Code Failure Prediction and Pattern Extraction using LSTM Networks

In this paper, we use a well-known Deep Learning technique called Long S...

Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder

Semiconductor lasers have been rapidly evolving to meet the demands of n...

Machine Learning-Driven Process of Alumina Ceramics Laser Machining

Laser machining is a highly flexible non-contact manufacturing technique...

An LSTM Network for Real-Time Odometry Estimation

The use of 2D laser scanners is attractive for the autonomous driving in...

Assessing Fatigue with Multimodal Wearable Sensors and Machine Learning

Fatigue is a loss in cognitive or physical performance due to various ph...