A new lifetime estimation model for a quicker LED reliability prediction

Hamon, B.; Mendizabal, L.; Feuillet, G.; Gasse, A.; Bataillou, B.

doi:10.1117/12.2060791

Cited by 1 publication

(1 citation statement)

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“…The model parameters were estimated with data obtained under accelerated life conditions. Philips Corp. [158] carried out a cross study of the evolution of electrical and optical characteristics. Models of the typical lumen depreciation and leakage resistance depreciation were made using electrical and optical measurements during the aging tests.…”

Section: Physics-based Methodsmentioning

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: Physics-based Methodsmentioning

confidence: 99%