TY - JOUR
T1 - Prognostics of radiation power degradation lifetime for ultraviolet light-emitting diodes using stochastic data-driven models
AU - Fan, Jiajie
AU - Jing, Zhou
AU - Cao, Yixing
AU - Ibrahim, Mesfin Seid
AU - Li, Min
AU - Fan, Xuejun
AU - Zhang, Guoqi
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Degradation modeling
KW - Gamma process
KW - IESNA TM-21
KW - Ultraviolet light-emitting diodes (UV LEDs)
KW - Wiener process
UR - http://www.scopus.com/inward/record.url?scp=85108161224&partnerID=8YFLogxK
U2 - 10.1016/j.egyai.2021.100066
DO - 10.1016/j.egyai.2021.100066
M3 - Article
AN - SCOPUS:85108161224
VL - 4
SP - 1
EP - 10
JO - Energy and AI
JF - Energy and AI
M1 - 100066
ER -