Prognostics of radiation power degradation lifetime for ultraviolet light-emitting diodes using stochastic data-driven models
Jiajie Fan,
Zhou Jing,
Yixing Cao,
Mesfin Seid Ibrahim,
Min Li,
Xuejun Fan,
Guoqi Zhang
Affiliations
Jiajie Fan
Institute of Future Lighting, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China; Shanghai Research Center for Silicon Carbide Power Devices Engineering & Technology, Fudan University, Shanghai 200433, China; EEMCS Faculty, Delft University of Technology, Delft 2628, the Netherlands; Changzhou Institute of Technology Research for Solid State Lighting, Changzhou 213161, China; Corresponding author at: Institute of Future Lighting, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China.
Zhou Jing
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Yixing Cao
Institute of Future Lighting, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
Mesfin Seid Ibrahim
Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
Min Li
Institute of Future Lighting, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China; Shanghai Research Center for Silicon Carbide Power Devices Engineering & Technology, Fudan University, Shanghai 200433, China
Xuejun Fan
Department of Mechanical Engineering, Lamar University, Beaumont, TX 77710, USA
Guoqi Zhang
EEMCS Faculty, Delft University of Technology, Delft 2628, the Netherlands
With their advantages of high efficiency, long lifetime, compact size and being free of mercury, ultraviolet light-emitting diodes (UV LEDs) are widely applied in disinfection and purification, photolithography, curing and biomedical devices. However, it is challenging to assess the reliability of UV LEDs based on the traditional life test or even the accelerated life test. In this paper, radiation power degradation modeling is proposed to estimate the lifetime of UV LEDs under both constant stress and step stress degradation tests. Stochastic data-driven predictions with both Gamma process and Wiener process methods are implemented, and the degradation mechanisms occurring under different aging conditions are also analyzed. The results show that, compared to least squares regression in the IESNA TM-21 industry standard recommended by the Illuminating Engineering Society of North America (IESNA), the proposed stochastic data-driven methods can predict the lifetime with high accuracy and narrow confidence intervals, which confirms that they provide more reliable information than the IESNA TM-21 standard with greater robustness.
