IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v154y2016icp152-159.html
   My bibliography  Save this article

Lumen degradation modeling of white-light LEDs in step stress accelerated degradation test

Author

Listed:
  • Huang, Jianlin
  • Golubović, DuÅ¡an S
  • Koh, Sau
  • Yang, Daoguo
  • Li, Xiupeng
  • Fan, Xuejun
  • Zhang, G.Q.

Abstract

In this paper, lumen degradation is described by using a modified Brownian motion process for mid-power white-light LED packages, which were aged under step stress accelerated degradation test (SSADT). First, a SSADT model has been established based on the theory of equivalent accumulative damage. Then, a method was proposed to improve the accuracy of the parameter estimation by carefully modifying the estimator, which was proposed in the previous research. Experimental data show that parameters estimated by using SSADT model are very close to those estimated by using constant stress accelerated degradation test (CSADT) model, indicating the feasibility of the SSADT model. The experiment also indicates that SSADT can be used as an alternative to CSADT, as it enables comparable estimation accuracy, while using less testing time, a smaller sample size and less test capacity.

Suggested Citation

  • Huang, Jianlin & Golubović, DuÅ¡an S & Koh, Sau & Yang, Daoguo & Li, Xiupeng & Fan, Xuejun & Zhang, G.Q., 2016. "Lumen degradation modeling of white-light LEDs in step stress accelerated degradation test," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 152-159.
  • Handle: RePEc:eee:reensy:v:154:y:2016:i:c:p:152-159
    DOI: 10.1016/j.ress.2016.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832016301107
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2016.06.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Molini, A. & Talkner, P. & Katul, G.G. & Porporato, A., 2011. "First passage time statistics of Brownian motion with purely time dependent drift and diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 1841-1852.
    2. Yashun Wang & Chunhua Zhang & Shufeng Zhang & Xun Chen & Yuanyuan Tan, 2015. "Optimal design of constant stress accelerated degradation test plan with multiple stresses and multiple degradation measures," Journal of Risk and Reliability, , vol. 229(1), pages 83-93, February.
    3. Wang, Xiaolin & Balakrishnan, Narayanaswamy & Guo, Bo, 2014. "Residual life estimation based on a generalized Wiener degradation process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 13-23.
    4. 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.
    5. Zhi‐Sheng Ye & Min Xie, 2015. "Stochastic modelling and analysis of degradation for highly reliable products," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(1), pages 16-32, January.
    6. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hanzhong Liu & Jiacai Huang & Yuanhong Guan & Li Sun, 2019. "Accelerated Degradation Model of Nonlinear Wiener Process Based on Fixed Time Index," Mathematics, MDPI, vol. 7(5), pages 1-16, May.
    2. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2018. "Nonlinear step-stress accelerated degradation modelling considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 207-215.
    3. Chen, Xingyu & Yang, Qingyu & Wu, Xin, 2022. "Nonlinear degradation model and reliability analysis by integrating image covariate," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Li, Junxing & Wang, Zhihua & Zhang, Yongbo & Liu, Chengrui & Fu, Huimin, 2018. "A nonlinear Wiener process degradation model with autoregressive errors," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 48-57.
    5. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.
    6. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Shirong & Tang, Yincai & Xu, Ancha, 2021. "A generalized Wiener process with dependent degradation rate and volatility and time-varying mean-to-variance ratio," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. 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.
    4. Zhai, Qingqing & Ye, Zhi-Sheng & Yang, Jun & Zhao, Yu, 2016. "Measurement errors in degradation-based burn-in," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 126-135.
    5. Kim, Seong-Joon & Mun, Byeong Min & Bae, Suk Joo, 2019. "A cost-driven reliability demonstration plan based on accelerated degradation tests," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 226-239.
    6. Le Liu & Xiao-Yang Li & Enrico Zio & Rui Kang & Tong-Min Jiang, 2017. "Model Uncertainty in Accelerated Degradation Testing Analysis," Post-Print hal-01652218, HAL.
    7. Dong, Qinglai & Cui, Lirong, 2019. "A study on stochastic degradation process models under different types of failure Thresholds," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 202-212.
    8. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank PA & Coolen-Maturi, Tahani, 2023. "New reliability model for complex systems based on stochastic processes and survival signature," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1349-1364.
    10. Yaping Li & Enrico Zio & Ershun Pan, 2021. "An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction," Journal of Risk and Reliability, , vol. 235(5), pages 831-844, October.
    11. Xudan Chen & Guoxun Ji & Xinli Sun & Zhen Li, 2019. "Inverse Gaussian–based model with measurement errors for degradation analysis," Journal of Risk and Reliability, , vol. 233(6), pages 1086-1098, December.
    12. Zhang, Jian-Xun & Si, Xiao-Sheng & Du, Dang-Bo & Hu, Chang-Hua & Hu, Chen, 2020. "A novel iterative approach of lifetime estimation for standby systems with deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    13. Wang, Huan & Wang, Guan-jun & Duan, Feng-jun, 2016. "Planning of step-stress accelerated degradation test based on the inverse Gaussian process," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 97-105.
    14. Liu, Bin & Liang, Zhenglin & Parlikad, Ajith Kumar & Xie, Min & Kuo, Way, 2017. "Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 200-209.
    15. Xu, Jun & Liang, Zhenglin & Li, Yan-Fu & Wang, Kaibo, 2021. "Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    16. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2019. "Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 261-270.
    17. Yan, Bingxin & Ma, Xiaobing & Yang, Li & Wang, Han & Wu, Tianyi, 2020. "A novel degradation-rate-volatility related effect Wiener process model with its extension to accelerated ageing data analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    18. Pang, Zhenan & Si, Xiaosheng & Hu, Changhua & Du, Dangbo & Pei, Hong, 2021. "A Bayesian Inference for Remaining Useful Life Estimation by Fusing Accelerated Degradation Data and Condition Monitoring Data," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    19. Cheng, Yao & Liao, Haitao & Huang, Zhiyi, 2021. "Optimal degradation-based hybrid double-stage acceptance sampling plan for a heterogeneous product," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    20. Dan Xu & Jiaolan He & Zhou Yang, 2022. "Reliability prediction based on Birnbaum–Saunders model and its application to smart meter," Annals of Operations Research, Springer, vol. 312(1), pages 519-532, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:154:y:2016:i:c:p:152-159. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.