The spectral power distribution (SPD) is the true fingerprint of a light source and is mainly dependent on electrical and thermal loading. Both the photometric and colorimetric quantities are originally extracted from SPD. Therefore, the dynamic prediction of SPD for LED has become an important aspect to evaluate the performance of LED during its time of operation. Generally, the statistical models are used to predict SPD. However, the statistical model with more than two input makes the system complex and time demanding. Artificial Neural Network (ANN) models, on the other hand, can help with this problem. The major goal of this research is to improve the utility of ANN in lighting applications. This is demonstrated by various neural network (NN) structures referred as models 1, 2 and 3 with combinations of varied neurons and hidden layers (HLs) to forecast SPD for various electrical and thermal stress levels at zero hours. The results are compared and based on absolute prediction error (APE) set to 5%, model 1 is considered as the best model for the SPD prediction. In addition, the time-based SPD prediction with model 1 is investigated using temperature, wavelength and time as input parameters for the LED luminaire and is validated.
PadmasaliANKiniSG. Prognostic algorithms for L70 life prediction of solid state lighting. Lighting Research and Technology2016; 48: 608–623.
3.
IES TM 21-11. Projecting Long Term Lumen Maintenance of LED Light Sources. Illuminating Engineering Society of North America: IES, 2011.
4.
IES LM-80-15. Approved Method for Lumen Maintenance Testing of LED Light Source. New York, NY, USA: Illuminating Engineering Society, 2015.
5.
PadmasaliANKiniSG. A lifetime performance analysis of LED luminaires under real-operation profiles. IEEE Transactions on Electron Devices2019; 67: 146–153.
6.
PadmasaliANKiniSG. Accelerated degradation test investigation for life-time performance analysis of LED luminaires. IEEE Transactions on Components, Packaging and Manufacturing Technology2019; 10: 551–558.
7.
Energy Star. Product Specification for Lamps (light bulbs). Washington DC, U.S.: Department of Energy, 2017.
8.
ANSI C78.377. Electric Lamps – Specifications for the Chromaticity of Solid State Lighting Products. Virginia, US: National Electrical Manufacturers Association, 2015.
9.
DurmusD. Correlated color temperature: use and limitations. Lighting Research and Technology2022; 54: 363–375.
10.
RoyerMWeiMWilkersonASafranekS. Experimental validation of colour rendition specification criteria based on ANSI/IES TM-30-18. Lighting Research and Technology2020; 52: 323–349.
11.
EspositoTHouserK. Models of colour quality over a wide range of spectral power distributions. Lighting Research and Technology2019; 51: 331–352.
12.
FanJJMohamedMGQianCFanXZhangGPechtM. Color shift failure prediction for phosphor-converted white LEDs by modeling features of spectral power distribution with a nonlinear filter approach. Materials2017; 10: 819.
13.
QianCFanJJFanXZhangG. Prediction of lumen depreciation and color shift for phosphor-converted white light-emitting diodes based on a spectral power distribution analysis method. IEEE Access2017; 5: 24054–24061.
14.
FanJJYuCQianCFanXZhangG. Thermal/luminescence characterization and degradation mechanism analysis on phosphor-converted white LED chip scale packages. Microelectronics Reliability2017; 74: 179–185.
15.
RaypahMEDevarajanMSulaimanF. Modeling spectra of low-power SMD LEDs as a function of ambient temperature. IEEE Transactions on Electron Devices2017; 64: 1180–1186.
SongBMHanB. Spectral power distribution deconvolution scheme for phosphor-converted white light-emitting diode using multiple Gaussian functions. Applied Optics2013; 52: 1016–1024.
18.
SongBMHanB. Analytical/experimental hybrid approach based on spectral power distribution for quantitative degradation analysis of phosphor converted LED. IEEE Transactions on Device and Materials Reliability2013; 14: 365–374.
19.
DupuisPPurwantoESinisukaNIZissisG. LED spectrum optimal modelization. 2018 IEEE Industry Applications Society Annual Meeting (IAS), Portland, OR, USA, 2018, pp. 1–12. IEEE.
20.
ChangM-HChaochaoCChen DasDPechtM. Anomaly detection of light-emitting diodes using the similarity-based metric test. IEEE Transactions on Industrial Informatics2014; 10: 1852–1863.
21.
Botero-ValenciaJSLopez-GiraldoFVargas-BonillaJF. Classification of artificial light sources and estimation of color rendering index using RGB sensors, k nearest neighbor and radial basis function. International Journal on Smart Sensing & Intelligent Systems2015; 8: 1505–1524.
22.
Botero-ValenciaJSValencia-AguirreJDurmusDDavisW. Multi-channel low-cost light spectrum measurement using a multilayer perceptron. Energy and Buildings2019; 199: 579–587.
23.
DeebRWeijerJvDMuseletDHebertMTremeauA. Deep spectral reflectance and illuminant estimation from self-interreflections. Journal of the Optical Society of America A2019; 36: 105–114.
24.
LuKZhangWSunB. Multidimensional data-driven life prediction method for white LEDs based on BP-NN and improved-adaboost algorithm. IEEE Access2017; 5: 21660–21668.
25.
FanJJLiYFrycIQianCFanXZhangG. Machine-learning assisted prediction of spectral power distribution for full-spectrum white light-emitting diode. IEEE Photonics Journal2019; 12: 1–18.
26.
SongYQianK. Research on the life prediction of light-emitting diode based on neural network. AIP Conference Proceedings. Chongqing City, China, 2017; vol. 1864: 020156. AIP Publishing LLC.
27.
LiuHGuoKZhangZYuDZhangJNingP, et al. High-power LED photoelectrothermal analysis based on backpropagation artificial neural networks. IEEE Transactions on Electron Devices2017; 64: 2867–2873.
28.
LiuHYuDNiuPZhangZGuoKWangD, et al. Lifetime prediction of a multi-chip high-power LED light source based on artificial neural networks. Results in Physics2019; 12: 361–367.
29.
CaoYYuanWChenWLiMFanJJZhangG. Predicting of luminous flux for a LED array using artificial neural network. 2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), Cracow, Poland, 2020, pp. 1–4. IEEE.
30.
YuanCCAFanJJFanX. Deep machine learning of the spectral power distribution of the LED system with multiple degradation mechanisms. Journal of Mechanics2021; 37: 172–183.
31.
Botero-ValenciaJSValencia AguirreJVargas BonillaJF. Estimation of light source color rendition with low-cost sensors using multilayer perceptron and extreme learning machine. Leukos2021; 17: 280–290.
BellJ. Machine learning: hands-on for developers and technical professionals, 2nd edition.John Wiley & Sons, New Jersey, US, 2020.
34.
QianCFanJJFanXChernyakovAEZhangGQ. Lumen and chromaticity maintenance lifetime prediction for LED lamps using a spectral power distribution method. 2015 12th China International Forum on Solid State Lighting (SSLCHINA), Shenzhen, China, 2015, pp. 67–70. IEEE.
