LED junction temperature prediction using machine learning techniques

Merenda, Massimo; Porcaro, Carlo; Corte, Francesco G. Della

doi:10.1109/melecon48756.2020.9140539

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…This strategy avoids the need for repeated PET and lifespan estimations. A novel method for estimating LED junction temperature using ML techniques is described in this work [32]. The study found that temperatures above 50 °C (50 °C-110 °C) have a better forecast with inaccuracy of 2 °C.…”

Section: Introductionmentioning

confidence: 99%

An analytical and machine learning model for SPD estimation and its prediction for lumen and chromaticity shift based LED lifetime performance analysis

Lokesh,

Padmasali,

Mahesha

et al. 2024

Phys. Scr.

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The LED lifetime is commonly estimated by manufacturers using an exponential model to evaluate L70 criteria. However, it ignores colour characteristic variation and does not explain the root cause of LED failure. In this paper, a spectral power distribution (SPD) based approach is proposed to estimate lifetime performance of cool white LED considering both colour characteristics and lumen maintenance, as all the lighting performance parameters are extracted from SPD. The exponential model does provide the lifetime using only lumen data and does not explain the colour characteristics. As an alternate to the exponential model, a quadratic polynomial, and machine learning (ML) models with hours and temperature as input factors, is proposed to determine SPD for experimental conditions as well as to predict for other operating conditions. Further, the lifetime performance analysis is performed for reliability assessment conditions through both lumen and colorimetric performances. The outcomes of all the models are analysed and it is found that the results are comparable. As ML models are simpler than analytical models for more than two inputs, further it is used to predict SPD at different temperatures and the LED performance is validated. Further analysis shows that a decrease in blue light is the primary cause of the overall decrease in light output and decrease in yellow emission due to phosphor degradation is the reason for chromaticity shift.

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Section: Introductionmentioning

confidence: 99%