Machine Learning based Data Driven Diagnostic and Prognostic Approach for Laser Reliability Enhancement

Abstract

In this paper, a data-driven diagnostic and prognostic approach based on machine learning is proposed to detect laser failure modes and to predict the remaining useful life (RUL) of a laser during its operation. We present an architecture of the proposed cognitive predictive maintenance framework and demonstrate its effectiveness using synthetic data.

2020 22nd International Conference on Transparent Optical Networks (ICTON)

References in corpus (2)

• Machine Learning based Laser Failure Mode Detection
• Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques

Cited by in corpus (2)

• Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory
• A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis