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- Title
Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes.
- Authors
Ibrahim, Mesfin Seid; Fan, Jiajie; Yung, Winco K. C.; Prisacaru, Alexandru; Driel, Willem; Fan, Xuejun; Zhang, Guoqi
- 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.
- Subjects
MACHINE learning; ARTIFICIAL neural networks; RELIABILITY in engineering; LIGHT sources; SUPPORT vector machines; LED displays
- Publication
Laser & Photonics Reviews, 2020, Vol 14, Issue 12, p1
- ISSN
1863-8880
- Publication type
Article
- DOI
10.1002/lpor.202000254