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

摘要

Light-emitting diodes (LEDs) are among the key innovations that haverevolutionized the lighting industry, due to their versatility in applications,higher reliability, longer lifetime, and higher efficiency compared with otherlight sources. The demand for increased lifetime and higher reliability hasattracted a significant number of research studies on the prognostics andlifetime estimation of LEDs, ranging from the traditional failure data analysisto the latest degradation modeling and machine learning based approachesover the past couple of years. However, there is a lack of reviews thatsystematically address the currently evolving machine learning algorithmsand methods for fault detection, diagnostics, and lifetime prediction of LEDs.To address those deficiencies, a review on the diagnostic and prognosticmethods and algorithms based on machine learning that helps to improvesystem performance, reliability, and lifetime assessment of LEDs is provided.The fundamental principles, pros and cons of methods including artificialneural 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 fromLED packages, components to system level reliability analysis, potentialchallenges and opportunities, and the future digital twin technology for LEDslifetime analysis is provided.