Machine Learning based Data Driven Diagnostic and Prognostic Approach for Laser Reliability Enhancement
arXiv:2203.11728v15 citationsh-index: 30
Originality Synthesis-oriented
AI Analysis
This addresses reliability enhancement for laser systems, but it appears incremental as it applies existing methods to a specific domain.
The paper tackles laser failure detection and remaining useful life prediction by proposing a machine learning-based diagnostic and prognostic approach, demonstrating its effectiveness with synthetic data.
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.