LED's luminous flux lifetime prediction using a hybrid numerical approach

Kijkanjanapaiboon, Kasemsak; Kretschmer, Theodore Wagner; Chen, Liangbiao; Fan, Xuejun; Zhou, Jiang

doi:10.1109/eurosime.2015.7103112

Cited by 5 publications

(2 citation statements)

References 12 publications

(10 reference statements)

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“…Cai et al [99] proposed a hybrid method for estimating the junction temperature of high-power LEDs at the system level by combining thermal modeling with temperature measurement. A hybrid numerical approach [120] was also presented to provide a way to predict the lifetime based on the maximum junction temperature of LED products, instead of running lumen maintenance tests at the system level to extrapolate the lifetime. Complete verification and successful real-life implementation of prognostic techniques are still big challenges.…”

Section: Validating the Prognostic Modelmentioning

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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Section: Validating the Prognostic Modelmentioning

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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“…An advanced lifetime prediction method was proposed in [5] which considered the field operation mission profile and statistical properties of the life data from accelerated degradation testing. In [6], a hybrid numerical approach based junction temperature analysis for LED luminous flux lifetime prediction was performed. In [7], the Bayesian method was proposed to estimate the lifetime gathering lumen degradation data.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Recommendations for LED Catastrophic Failure Due to Voltage Stress

2022

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Light-emitting diode (LED) lighting has, compared to other types of lighting, a significantly lower energy consumption. However, the perceived service life is also important for customer satisfaction and here there is a discrepancy between customers’ experience and manufacturers’ statements. Many customers experience a significantly shorter service life than claimed by the manufacturers. An experiment was carried out in the Pehr Högström Laboratory at Luleå University of Technology in Skellefteå, Sweden to investigate whether voltage disturbances could explain this discrepancy. Over 1000 LED lamps were exposed to high levels of voltage disturbances for more than 6000 h; the failure rate from this experiment was similar to the one from previous experiments in which lamps were exposed to normal voltage. The discrepancy thus remains, even though some possible explanations have emerged from the project’s results. The lamps were exposed to five different types of voltage disturbances: short interruptions; transients; overvoltage; undervoltage; and harmonics. Only overvoltage resulted in failure of the lamps, and only for a single topology of lamp. A detailed analysis has been made of the topology of lamps that failed. This lamp type contains a different internal electronics circuit than the other lamp types. Failures of the lamps when exposed to overvoltage are due to the heat development in the control circuit increasing sharply when the lamps are exposed to a higher voltage. Hence, it is concluded that there are lamps that are significantly more sensitive to voltage disturbances than other lamp types. Manufactures need to consider the voltage quality that can be expected at the terminal of the lamp to prevent failure of lamps due to voltage disturbances. This paper therefore contains recommendations for manufacturers of lighting; the recommendations describe which voltage disturbances lamps should cope with.

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