Health Monitoring, Machine Learning, and Digital Twin for LED Degradation Analysis
Mesfin Seid Ibrahim (The Hong Kong Polytechnic University, Wollo University)
Zhou Jing (Hohai University)
Jiajie Fan (Hohai University, TU Delft - Electronic Components, Technology and Materials, Fudan University)
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 analysis and machine learning-based approaches over the past couple of years. However, there have been few reviews that systematically address the currently evolving machine learning (ML) algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address these deficiencies, we provide 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. And the emerging trend in the application of digital twins for PHM with the focus on LEDs is also discussed. Finally, a case study on UV LED radiation degradation modeling with different machine learning methods is provided.