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Prognostics of lumen maintenance for High power white light emitting diodes using a nonlinear filter-based approach

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  • Fan, Jiajie
  • Yung, Kam-Chuen
  • Pecht, Michael

Abstract

High power white light emitting diodes (HPWLEDs), with advantages in terms of luminous efficacy, energy saving, and reliability, have become a popular alternative to conventional luminaires as white light sources. Like other new electronic products, HPWLEDs must also undergo qualification testing before being released to the market. However, most traditional qualification tests, which require all devices under testing to fail, are time-consuming and expensive. Nowadays, as recommended by the Illuminating Engineering Society (IES, IES-TM-21-11), many LED manufacturers use a projecting approach based on short-term collected light output data to predict the future lumen maintenance (or lumen lifetime) of LEDs. However, this projecting approach, which depends on the least-square regression method, generates large prediction errors and uncertainties in real applications. To improve the prediction accuracy, we present in this paper a nonlinear filter-based prognostic approach (the recursive Unscented Kalman Filter) to predict the lumen maintenance of HPWLEDs based on the short-term observed data. The prognostic performance of the proposed approach and the IES-TM-21-11 projecting approach are compared and evaluated with both accuracy- and precision-based metrics.

Suggested Citation

  • Fan, Jiajie & Yung, Kam-Chuen & Pecht, Michael, 2014. "Prognostics of lumen maintenance for High power white light emitting diodes using a nonlinear filter-based approach," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 63-72.
    • Handle: RePEc:eee:reensy:v:123:y:2014:i:c:p:63-72
    DOI: 10.1016/j.ress.2013.10.005
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    References listed on IDEAS

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    1. Zio, Enrico & Peloni, Giovanni, 2011. "Particle filtering prognostic estimation of the remaining useful life of nonlinear components," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 403-409.
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    1. Zhiguo Zeng & Francesco Di Maio & Enrico Zio & Rui Kang, 2017. "A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods," Journal of Risk and Reliability, , vol. 231(1), pages 36-52, February.
    2. Sun, Bo & Fan, Xuejun & Ye, Huaiyu & Fan, Jiajie & Qian, Cheng & van Driel, Williem & Zhang, Guoqi, 2017. "A novel lifetime prediction for integrated LED lamps by electronic-thermal simulation," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 14-21.
    3. Rocchetta, Roberto & Zhan, Zhouzhao & van Driel, Willem Dirk & Di Bucchianico, Alessandro, 2024. "Uncertainty analysis and interval prediction of LEDs lifetimes," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Rocco Sanseverino, Claudio M. & Ramirez-Marquez, José Emmanuel, 2014. "Uncertainty propagation and sensitivity analysis in system reliability assessment via unscented transformation," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 176-185.
    5. Qian, C. & Fan, X.J. & Fan, J.J. & Yuan, C.A. & Zhang, G.Q., 2016. "An accelerated test method of luminous flux depreciation for LED luminaires and lamps," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 84-92.
    6. Sun, Bo & Fan, Xuejun & van Driel, Willem & Cui, Chengqiang & Zhang, Guoqi, 2018. "A stochastic process based reliability prediction method for LED driver," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 140-146.

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