Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light-Emitting Diodes
Mesfin Seid Ibrahim (The Hong Kong Polytechnic University, Wollo University)
Jiajie Fan (Fudan University, Hohai University)
Winco K.C. Yung (The Hong Kong Polytechnic University)
Alexandru Prisacaru (Robert Bosch GmbH)
Willem van Driel (Signify, TU Delft - Electronic Components, Technology and Materials)
Xuejun Fan (TU Delft - Electronic Components, Technology and Materials, Lamar University)
Guoqi Zhang (TU Delft - Electronic Components, Technology and Materials)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.