Abstract
With the expanding application of light-emitting diodes (LEDs), the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD), defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED’s optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1) the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2) the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs), and color rendering indexes (CRIs) of phosphor-converted (pc)-white LEDs, and also can estimate the residual color life.
Highlights
Artificial lighting consumes around 19% of the world’s total energy, which produces approximately 10% of all carbon emitted in the world [1,2]
1% with the proposed particle filter (PF) approach, in which the approach with the Gaussian model has the better prediction accuracy in u′ and that with the Lorentzian model has less prediction error in v′; (2) the prediction accuracy in u0 and that with the Lorentzian model has less prediction error in v0 ; (2) the prediction errors of both the correlated color temperatures (CCTs) and color rendering indexes (CRIs) can be controlled under 5%
Prediction errors of both the CCTs and CRIs can be controlled under 5%
Summary
Artificial lighting consumes around 19% of the world’s total energy, which produces approximately 10% of all carbon emitted in the world [1,2]. As reviewed, few of the current studies on SPDs can combine the failure mechanism analysis and residual color lifetime prediction together to assess the reliability of pc-WLEDs during degradation testing. To investigate the color shift failure mechanisms and predict the residual color lifetime for a pc-WLED aged under a degradation test, this paper proposes a model-based prognostic method by extracting the features of an SPD with the statistical functions with both Gaussian and Lorentzian model firstly, and modeling the shift trajectories of features of SPDs with a nonlinear filtering approach.
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