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A stochastic process based reliability prediction method for LED driver

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  • Sun, Bo
  • Fan, Xuejun
  • van Driel, Willem
  • Cui, Chengqiang
  • Zhang, Guoqi

Abstract

In this study, we present a general methodology that combines the reliability theory with physics of failure for reliability prediction of an LED driver. More specifically, an integrated LED lamp, which includes an LED light source with statistical distribution of luminous flux, and a driver with a few critical components, is considered. The Wiener process is introduced to describe the randomness of lumen depreciation. The driver's survival probability is described using a general Markov Chain method. The system compact thermal model (physics of failure model) is developed to couple with the reliability methods used. Two scenarios are studied: Scenario S1 considers constant driver's operation temperature, while Scenario S2 considers driver's temperature rise due to lumen depreciation. It has been found that the wide life distribution of LEDs will lead to a large range of the driver's survival probability. The proposed analysis provides a general approach for an electronic system to integrate the reliability method with physics models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:178:y:2018:i:c:p:140-146
    DOI: 10.1016/j.ress.2018.06.001
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    References listed on IDEAS

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    1. Fan, Mengfei & Zeng, Zhiguo & Zio, Enrico & Kang, Rui & Chen, Ying, 2018. "A stochastic hybrid systems model of common-cause failures of degrading components," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 159-170.
    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. Zhang, Chunhua & Lu, Xiang & Tan, Yuanyuan & Wang, Yashun, 2015. "Reliability demonstration methodology for products with Gamma Process by optimal accelerated degradation testing," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 369-377.
    4. Chen, Ying & Yang, Liu & Ye, Cui & Kang, Rui, 2015. "Failure mechanism dependence and reliability evaluation of non-repairable system," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 273-283.
    5. Lamoureux, Benjamin & Mechbal, Nazih & Massé, Jean-Rémi, 2014. "A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 12-26.
    6. 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.
    7. Kostandyan, Erik E. & Sørensen, John D., 2012. "Physics of failure as a basis for solder elements reliability assessment in wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 100-107.
    8. 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.
    9. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
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