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

TL;DR

This paper introduces a machine learning-based data-driven framework for diagnosing laser failures and predicting their remaining useful life, aiming to improve laser reliability through predictive maintenance.

Contribution

It presents a novel cognitive predictive maintenance architecture and demonstrates its effectiveness using synthetic data, advancing laser failure prognosis methods.

Findings

Effective detection of laser failure modes

Accurate prediction of remaining useful life

Validated framework using synthetic data

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.