Skip to main content
Log in

Luminance degradation test and life prediction of LED light at conventional stress using TPWRAM

  • Published:
Journal of Materials Science: Materials in Electronics Aims and scope Submit manuscript

Abstract

With the rapid development of the light emitting diode (LED) industry, the issue of its life has also attracted significant attention. How to accurately and quickly obtain the conventional life of LED light has become an urgent problem to be solved in recent years. In this work, a life test based on conventional stress was carried out to gain the average luminance degradation data for samples with time, and then set up life prediction model based on three-parameter Weibull function and right approximation method to process test data. The average life of the samples was calculated in combination with the failure criteria, and the life prediction of LED light was achieved. The results indicate that the average luminance degradation data obtained by the life test under conventional stress basically shows a nonlinear decay trend, which is in line with the luminance degradation law of general optoelectronic devices. The determination coefficient for the fitted curve of three-parameter Weibull right approximation method (TPWRAM) is 0.9994, which is very close to 1, indicating that the method has a high level of fitting accuracy. The mean relative error of average luminance degradation data under conventional stress is 0.559%, demonstrating that TPWRAM has high accuracy in predicting the conventional life of LED light. The average life of the samples was deduced, which can be used to verify the effectiveness of the assumptions proposed in the accelerated degradation test and provide crucial guidance for LED light production.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. D.Y. Shin, W.G. Shin, H.M. Hwan, G.H. Kang, Grid-type LED media façade with reflective walls for building-integrated photovoltaics with virtually no shading loss. Appl. Energy 332, 120553 (2023)

    Article  CAS  Google Scholar 

  2. T. Pavel, M. Konstantin, S. Arseniy, B. Vadim, S. Alexander, S. Alexander, LED lighting agrosystem with parallel power supply from photovoltaic modules and a power grid. Agric. 12(8), 1208–1215 (2022)

    Google Scholar 

  3. Y. Ye, W. Zhang, Y. Zhang, CsPbBr 3 nanocrystals embedded glass enables highly stable and efficient light-emitting diodes. Chem. Eng. J. 445, 136867 (2022)

    Article  CAS  Google Scholar 

  4. Y.C. Liu, C.S. Li, Z.J. Ren, S.K. Yan, M.R. Bryce, All-organic thermally activated delayed fluorescence materials for organic light-emitting diodes. Nat. Rev. Mater. 3(4), 18020 (2018)

    Article  CAS  Google Scholar 

  5. M. Anaya, B.P. Rand, R.J. Holmes, D. Credgington, H.J. Bolink, R.H. Friend, J.P. Wang, N.C. Greenham et al., Best practices for measuring emerging light-emitting diode technologies. Nat. Photonics 13(12), 818–821 (2019)

    Article  CAS  Google Scholar 

  6. A. Esraa, M.E. Hazem, A. Ahmed, E. Sara, An innovative traffic light recognition method using vehicular ad-hoc networks. Sci. Rep. 13(1), 4009 (2023)

    Article  Google Scholar 

  7. V. Balasubramanian, R. Bhardwaj, Pedestrians’ perception and response towards vehicles during road-crossing at nighttime. Accid. Anal. Prev. 110, 128–135 (2018)

    Article  Google Scholar 

  8. V. Lodovica, F. Francesca, P. Anna, Renovation of public lighting systems in cultural landscapes: lighting and energy performance and their impact on nightscapes. Energies 14(2), 509 (2021)

    Article  Google Scholar 

  9. L.H. Hu, J. Choi, S. Hwangbo, D. Kwon, B. Jang, S. Ji, J.H. Kim, S.K. Han et al., Flexible micro-LED display and its application in Gbps multi-channel visible light communication. NPJ Flex. Electron. 6(1), 100 (2022)

    Article  Google Scholar 

  10. M.S. Ibrahim, Z. Jing, W.K.C. Yung, J.J. Fan, Bayesian based lifetime prediction for high-power white LEDs. Expert Syst. Appl. 185, 115627 (2021)

    Article  Google Scholar 

  11. B. Han, B.B. Liu, Y.Z. Dai, J. Zhang, H.Z. Shi, Development of a novel yellow-emitting niobate phosphor for white light emitting diodes. J. Mater. Sci. Mater. Electron. 30(12), 11145–11150 (2019)

    Article  CAS  Google Scholar 

  12. S.D. Huang, S.L. Zhou, G.Z. Cao et al., A novel multiple-stress-based predictive model of LEDs for rapid lifetime estimation. Microelectron. Reliab. 78, 46–52 (2017)

    Article  Google Scholar 

  13. X.H. Qu, H. Wang, X.Q. Zhan et al., A lifetime prediction method for LEDs considering real mission profiles. IEEE Trans. Power Electr. 32, 8718–8727 (2017)

    Article  Google Scholar 

  14. N. Zhao, Z.L. Li, L.D. Qin, Z.J. Cui, Z. Sun, Z.Y. Cheng, C.S. Jiang, S.S. Wang et al., Lifetime measurement and aging mechanism analysis of OLED subpixels. Displays 75, 102326 (2022)

    Article  Google Scholar 

  15. W.P. Diao, S. Saxena, M. Pecht, Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells. J. Power. Sources 435, 226830 (2019)

    Article  CAS  Google Scholar 

  16. J.P. Zhang, K.R. Xue, Q. Zhou, J. Fu, Z.W. Zhang, H.B. Wang, J. Hu, Z.B. Qu, A life testing system design and life prediction for plant lighting LED-based luminaires. Opt. Mater. 132, 112803 (2022)

    Article  CAS  Google Scholar 

  17. M.S. Ibrahim, Z. Jing, W.K.C. Yung et al., Bayesian based lifetime prediction for high-power white LEDs. Expert Syst. Appl. 185, 115627 (2021)

    Article  Google Scholar 

  18. K.Y. Lu, W.J. Zhang, B. Sun, Multidimensional data-driven life prediction method for white LEDs based on BP-NN and improved-adaboost algorithm. IEEE Access 5, 21660–21668 (2017)

    Article  Google Scholar 

  19. A. Padmasali, S. Kini, LED life prediction based on lumen depreciation and colour shift. Lighting Res. Technol. 49, 84–99 (2017)

    Article  Google Scholar 

  20. X.Y. Li, L. Zhang, Z.P. Wang, P. Dong, Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. J. Energy Storage 21, 510–518 (2019)

    Article  Google Scholar 

  21. V.N. Popok, S. Buhrkal-Donau, B. Czerny, G. Khatibi, H. Luo, F. Iannuzzo, K.B. Pedersen, Comparative study of wire bond degradation under power and mechanical accelerated tests. J. Mater. Sci. Mater. Electron. 30(18), 17040–17045 (2019)

    Article  CAS  Google Scholar 

  22. B. Bora, S. Mondal, B. Prasad, O.S. Sastry, M. Bangar, A.K. Tripathi, C. Banerjee, Accelerated stress testing of potential induced degradation susceptibility of PV modules under different climatic conditions. Sol. Energy 223, 158–167 (2021)

    Article  CAS  Google Scholar 

  23. F.J. Oldenburg, A. Ouarga, T.J. Schmidt, L. Gubler, Accelerated stress test method for the assessment of membrane lifetime in vanadium redox flow batteries. ACS Appl. Mater. Interfaces 11(5), 47917–47928 (2019)

    Article  CAS  Google Scholar 

  24. A.M. Titu, A.A. Boroiu, A. Boroiu, M. Dragomir, A.B. Pop, S. Titu, Reliability modelling through the three-parametric Weibull model based on Microsoft excel facilities. Processes 10(8), 1585 (2022)

    Article  Google Scholar 

  25. L.S. Wang, Y.Y. Fang, T. Zhao, J.T. Wang, H. Zhang, L. Wang, S.G. Lu, Lithium-ion cell inconsistency analysis based on three-parameter Weibull probability model. Rare Met. 39(4), 392–401 (2020)

    Article  CAS  Google Scholar 

  26. J.P. Zhang, X. Zhang, Y. Zong, Y.F. Pan, H.L. Wu, J.S. Tang, Life prediction for a vacuum fluorescent display based on two improved models using the three-parameter Weibull right approximation method. Luminescence 33(1), 34–39 (2018)

    Article  CAS  Google Scholar 

  27. X. Jia, Reliability analysis for Weibull distribution with homogeneous heavily censored data based on Bayesian and least-squares methods. Appl. Math. Model. 83, 169–188 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This work is sponsored by the Program of Foundation of Science and Technology Commission of Shanghai Municipality (22dz1206005, 22dz1204202), National Natural Science Foundation of China (12172228, 11572187), Natural Science Foundation of Shanghai (22ZR1444400), and Shanghai Professional Technical Service Platform for Intelligent Operation and Maintenance of Renewable Energy (22DZ2291800).

Funding

Funding was provided by Science and Technology Commission of Shanghai Municipality (Grant Nos. 22dz1206005, 22dz1204202), National Natural Science Foundation of China (Grant Nos. 12172228, 11572187), Natural Science Foundation of Shanghai (Grant No. 22ZR1444400) and Shanghai Professional Technical Service Platform for Intelligent Operation and Maintenance of Renewable Energy (Grant No. 22DZ2291800).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Conceptualization, methodology, supervision, project administration, funding acquisition, resources were performed by ZJ. Methodology, data curation, writing-original draft & editing were finished by ZY, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jianping Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this work.

Ethical approval

This article does not contain any studies involving animals performed by any of the authors. Also, this article does not contain any studies involving human participants performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Zhang, Y. Luminance degradation test and life prediction of LED light at conventional stress using TPWRAM. J Mater Sci: Mater Electron 34, 2197 (2023). https://doi.org/10.1007/s10854-023-11531-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10854-023-11531-2

Navigation