Modulation method for measurement of thermal resistance of high-power COB LEDs

Смирнов, В.И.; Sergeev, Viacheslav A.; Gavrikov, A.A.; Shorin, A.M.

doi:10.1109/therminic.2017.8233815

Cited by 1 publication

(1 citation statement)

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“…This technology makes it possible to place multichips in a small area in order to create a multichip LED structure [12]. LED lighting fixtures of approximately 500 W power and 60,000 lm luminous flux can be produced by securing good thermal conductivity with the help of modern thermal conductive adhesives [13,14].…”

Section: Introductionmentioning

confidence: 99%

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Kıyak

Gökmen

Koçyiğit

2021

Journal of Nanomaterials

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Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

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Section: Introductionmentioning

confidence: 99%