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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.

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Miniaturization of electronics, reduction of time to market and new functionalities in the current context of autonomous driving, electrification and connectivity, are bringing new reliability challenges. Prognostics and Health Management (PHM) can be used effectively to address some of the key challenges, in particular new challenges associated with the transfer of consumer electronics to automotive industry. The concept of PHM is not new, but its application to electronic systems is relatively new. It is expected that the PHM demand for electronic systems would continuously increase as autonomous driving is being realized. This paper attempts to summarize the recent studies in the system-level PHM of electronic systems. Condition monitoring (CM) techniques and prognostics methods used for the PHM of electronic systems are reviewed first. Various implementation examples are followed using different system classifications. The findings from this review is expected to offer a technical summary of accomplishments and challenges during the course of applying PHM for electronic systems, and to identify future research tasks to be performed to make the PHM a more viable tool for reliability assessment of electronic systems.