Jiajie Fan 1,2,3, Moumouni Guero Mohamed 1, Cheng Qian 2, Xuejun Fan 2,4, Guoqi Zhang 2,3,5 and Michael G. Pecht 6
1 College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
2 Changzhou Institute of Technology Research for Solid State Lighting, Changzhou 213161, China
3 Beijing Research Center, Delft University of Technology, Delft 2628, The Netherlands
4 Department of Mechanical Engineering, Lamar University, Beaumont, TX 77710, USA
5 EEMCS Faculty, Delft University of Technology, Delft 2628, The Netherlands
6 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
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.