Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light-Emitting Diodes

Mesfin Seid Ibrahim, Jiajie Fan, Winco K.C. Yung, Alexandru Prisacaru, Willem van Driel, Xuejun Fan, Guoqi Zhang

Research output: Journal article publicationReview articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Light-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime, and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation modeling and machine learning based approaches over the past couple of years. However, there is a lack of reviews that systematically address the currently evolving machine learning algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address those deficiencies, a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability, and lifetime assessment of LEDs is provided. The fundamental principles, pros and cons of methods including artificial neural networks, principal component analysis, hidden Markov models, support vector machines, and Bayesian networks are presented. Finally, discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability analysis, potential challenges and opportunities, and the future digital twin technology for LEDs lifetime analysis is provided.

Original languageEnglish
Article number2000254
JournalLaser and Photonics Reviews
Volume14
Issue number12
DOIs
Publication statusPublished - Dec 2020

Keywords

  • data-driven methods
  • diagnostics and prognostics
  • digital twins
  • light-emitting diodes (LEDs)
  • machine learning (ML) algorithms
  • statistical methods

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics

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