Illumination Variation Similarity Based Fault Diagnosis for HV-LED Lamp Driven by Segmented Linear Driver

  • Fukang SunEmail author
  • Shaofeng Zhu
  • Ye Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)


In this paper, fault diagnosis methods based on illumination variation similarity analysis for High-voltage Light Emitting Diode (HV-LED) lamp driven by segmented linear driver are proposed. The proposed methods assess the illumination variation similarity between the diagnosed lamp and the different fault-type lamp to diagnose whether the diagnosed lamp occurs fault or not and confirm the fault type. Euclidean distance is applied for calculating illumination variation similarity. The proposed fault diagnosis methods contain four parts: illumination signal smoothing, similarity calculation, similarity assessment, and fault recognition. The illumination variation data of the fault-free and three different faulty lamps, which are based on one HV-LED lamp driven by four-segment linear driver, are investigated for method verification experiments. The experimental results and analysis are given to demonstrate the validity and effectiveness of the proposed fault diagnosis methods in test chamber environment.


HV-LED lamp Segmented linear driver Fault diagnosis method Illumination variation characteristics Similarity analysis Euclidean distance 



This work was supported by the Natural Science Research Project of Anhui Education Department (KJ2015JD24 and KJ2016A814 project), the Research Project of Anhui Provincial Housing and Urban Construction Department (2015YF-18 project), and the Research Project of Anhui Jianzhu University (2014XQZ03 project and 2017XQZ01 project).


  1. 1.
    Steigerwald, D.A., Bhat, J.C., et al.: Illumination with solid state lighting technology. IEEE J. Sel. Top. Quantum Electron. 8(2), 310–320 (2002)CrossRefGoogle Scholar
  2. 2.
    Hwu, K.I., Tu, W.C.: A high brightness light-emitting diode driver with power factor and total harmonic distortion improved. In: 26th IEEE Applied Power Electronics Conference & Exposition, pp. 713–717. IEEE, USA (2011)Google Scholar
  3. 3.
    Jong-hyun, K.I.M., Myung-hyo, R.Y.U., et al.: A new dimmer for alternating-current directly driven light-emitting-diode lamp. J. Cent. South Univ. 19(2), 374–379 (2012)CrossRefGoogle Scholar
  4. 4.
    Dayal, R., Modepalli, K., Parsa, L.: A direct AC LED driver with high power factor without the use of passive components. In: 2012 IEEE Energy Conversion Congress and Exposition, pp. 4230–4234. IEEE, USA (2012)Google Scholar
  5. 5.
    Park, C., Rim, C.T.: Filter-free AC direct LED driver with unity power factor and low input current THD using binary segmented switched LED strings and linear current regulator. In: 28th Applied Power Electronics Conference and Exposition, pp. 870–874. IEEE, USA (2013)Google Scholar
  6. 6.
    Ning, N., Chen, W.B., Yu, D.J., et al.: Self-adaptive load technology for multiple-string LED drivers. Electron. Lett. 49(18), 1170–1171 (2013)CrossRefGoogle Scholar
  7. 7.
    Seo, K., Nguyen, V.H., Jung, J., et al.: Multi-string AC-powered LED driver with current regulation reduction based on simple circuitry. IEICE Electron. Express 11(19), 1–8 (2014)CrossRefGoogle Scholar
  8. 8.
    Li, Y., Guo, W., Zhu, Z.: A high efficiency and power factor segmented linear constant current LED driver. China J. Semicond. 36(4), 165–171 (2015)CrossRefGoogle Scholar
  9. 9.
    Liu, C., Lai, X., Hanxiao, D.: Improvements in performance and reliability for segmented linear LED drivers. China J. Semicond. 37(7), 41–47 (2016)CrossRefGoogle Scholar
  10. 10.
    Noge, Y.: Linear AC LED driver with the multi-level structure and variable current regulator. In: 9th International Conference on Power Electronics and Ecce Asia, pp. 964–969. IEEE, South Korea (2015)Google Scholar
  11. 11.
    Noge, Y., Fuse, H., Shimizu, T.: Experimental validation of linear AC LED driver with quantitative design method. In: 2017 Applied Power Electronics Conference & Exposition, pp. 1484–1491. IEEE, USA (2017)Google Scholar
  12. 12.
    Huang, H.P., Li, C., Jeng, J.C.: Multiple multiplicative fault diagnosis for dynamic processes via parameter similarity measures. Ind. Eng. Chem. Res. 46(13), 4517–4530 (2007)Google Scholar
  13. 13.
    Liu, C., Jiang, D., Yang, W.: Global geometric similarity scheme for feature selection in fault diagnosis. Expert Syst. Appl. 41(8), 3585–3595 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Anhui Province Key Laboratory of Intelligent Building and Building Energy-SavingAnhui Jianzhu UniversityHefeiChina
  2. 2.Engineering Research Center for Building Energy Efficiency Control and Evaluation, Ministry of EducationHefeiChina
  3. 3.Anhui Engineering Technology Research Center for Building Energy-SavingHefeiChina

Personalised recommendations