A systematic approach for reliability assessment of electrolytic capacitor-free LED drivers

Sun, Bo; Fan, Xuejun; Driel, W.D. van; Zhang, Guoqi

doi:10.1109/eurosime.2016.7463387

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Recently, Sun et al [253] developed a PoF-based reliability prediction method to estimate the failure rate distribution of electrolytic capacitors used in LED drivers with considering the temperature effect of electrolytic capacitors under operation conditions. Furthermore, Sun et al [254] also applied the reliability assessment method on the electrolytic capacitor-free LED drivers to investigate the failure rate of MOSFETs in the drivers. Comparatively little research has been conducted on the prognostics of LED drivers [172] [173].…”

Section: Led System-level Prognosticsmentioning

confidence: 99%

A Review of Prognostic Techniques for High-Power White LEDs

Sun

Jiang

Yung

et al. 2017

IEEE Trans. Power Electron.

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High-power white light-emitting diodes (LEDs) have attracted much attention due to their versatility in a variety of applications and growing demand in markets such as general lighting, automotive lamps, communications devices, and medical devices. In particular, the need for high reliability and long lifetime poses new challenges for the research and development, production, and application of LED lighting. Accurate and effective prediction of the lifetime or reliability of LED lighting has emerged as one of the key issues in the solid-state lighting field. Prognostics is an engineering technology that predicts the future reliability or determines the remaining useful lifetime (RUL) of a product by assessing the extent of deviation or degradation of a product from its expected normal operating conditions. Prognostics bring benefits to both LED developers and users, such as optimizing system design, shortening qualification test times, enabling condition-based maintenance for LED-based systems, and providing information for return-on-investment (ROI) analysis. This paper provides an overview of the prognostic methods and models that have been applied to both LED devices and LED systems, especially for use in long-term operational conditions. These methods include statistical regression, static Bayesian network, Kalman filtering, particle filtering, artificial neural network, and physics-based methods. The general concepts and main features of these methods, the advantages and disadvantages of applying these methods, as well as LED application case studies, are discussed. The fundamental issues of prognostics and photo-electro-thermal (PET) theory for LED systems are also discussed for clear understanding of the reliability and lifetime concepts for LEDs. Finally, the challenges

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