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Application of double Q wavelet-based sparse decomposition to fault feature extraction of wind turbine planetary gearbox

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The wind turbine gearbox is a critical equipment transforming the speed of the rotor hub to the generator, the condition of which is the reflection of operational efficiency and reliability of wind turbines. As the initial stage of the wind turbine gearbox, the fault feature extraction of the planetary gear set is challenging since it is prone to be affected by complicated structure, vibration from other high-speed stages and background noise. In this paper, a double Q factor wavelet-based sparse decomposition is applied to the fault feature extraction of the wind turbine planetary gearbox. Considering the sparsest wavelet coefficients, the vibration signal is iteratively decomposed into high Q and low Q components. The fault feature is generally hidden in the low Q component. With further demodulation, the fault information of planetary gears can be easily detected.
Rocznik
Strony
353--371
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
  • China Green Development Investment Group CO. LTD. Beijing 100020, China
  • Luneng Group CO. LTD. Beijing, 100020, China
autor
  • China Green Development Investment Group CO. LTD. Beijing 100020, China
  • ) Luneng Group CO. LTD. Beijing, 100020, China
autor
  • Jiangsu Goldwind Science & Technology CO. LTD. Yancheng, 224100, China
autor
  • Jiangsu Goldwind Science & Technology CO. LTD. Yancheng, 224100, China
Bibliografia
  • 1. http://www.xinhuanet.com/english/2021-07/12/c 1310057114.htm, accessed August 18, 2021.
  • 2. https://gwec.net/global-wind-report-2021/ Global wind report 2021.
  • 3. Feng Z., Zuo M.J., Vibration signal models for fault diagnosis of planetary gearboxes, Journal of Sound and Vibration, 331(22): 4919–4939, 2012, doi: 10.1016/j.jsv.2012.05.039.
  • 4. Feng Z., Chen X., Liang M., Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions, Mechanical Systems and Signal Processing, 52–53: 360–375, 2015, doi: 10.1016/j.ymssp.2014.07.009.
  • 5. Feng Z., Liang M., Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time-frequency analysis, Renewable Energy, 66: 468–477, 2014, doi: 10.1016/j.renene.2013.12.047.
  • 6. Lei Y., Han D., Lin J., He Z., Planetary gearbox fault diagnosis using an adaptive stochastic resonance method, Mechanical Systems and Signal Processing, 38(1): 113–124, 2013, doi: 10.1016/j.ymssp.2012.06.021.
  • 7. Zhang J., Zhong M., Zhang J.Q., Yao L.G., Zheng J.D., An integrating methodology of Teager energy operator and stochastic resonance for incipient fault diagnosis of planetary gearbox [in Chinese], Journal of Vibration Engineering, 32(6): 1084–1093, 2019.
  • 8. Liang X., Zuo M. J., Hoseini M.R., Vibration signal modeling of a planetary gear set for tooth crack detection, Engineering Failure Analysis, 48: 185–200, 2015, doi: 10.1016/j.engfailanal.2014.11.015
  • 9. Selesnick I.W., Resonance-based signal decomposition: a new sparsity-enabled signal analysis method, Signal Processing, 91(12): 2793–2809, 2011, doi: 10.1016/j.sigpro.2010. 10.018.
  • 10. Selesnick I.W., Wavelet transform with tunable Q-factor, IEEE Transactions on Signal Processing, 59(8): 3560–3575, 2011, doi: 10.1109/TSP.2011.2143711.
  • 11. Cai G.G., Chen X.F., He Z.J., Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox, Mechanical Systems and Signal Processing, 41(1–2): 34–53, 2013, doi: 10.1016/j.ymssp.2013.06.035.
  • 12. Du Z., Chen X., Zhang H., Yan R., Sparse feature identification based on union of redundant dictionary for wind turbine gearbox fault diagnosis, IEEE Transactions on Industrial Electronics, 62(10): 6594–6605, 2015, doi: 10.1109/TIE.2015.2464297.
  • 13. He W., Chen B., Zeng N., Zi Y., Sparsity-based signal extraction using dual Q-factors for gearbox fault detection, ISA Transactions, 79: 147–160, 2018, doi: 10.1016/j.isatra.2018.05.009.
  • 14. Teng W., Liu Y., Huang Y., Song L., Liu Y., Ma Z., Fault detection of planetary subassemblies in a wind turbine gearbox using TQWT based sparse representation, Journal of Sound and Vibration, 490: 115707, 2021, doi: 10.1016/j.jsv.2020.115707.
  • 15. Selesnick I., Sparse regularization via convex analysis, IEEE Transactions on Signal Processing, 65(17): 4481–4494, 2017, doi: 10.1109/TSP.2017.2711501.
  • 16. Cai G., Selesnick I.W., Wang S., Dai W., Zhu Z., Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis, Journal of Sound and Vibration, 432: 213–234, 2018, doi: 10.1016/j.jsv.2018.06.037.
  • 17. Wang L., Cai G., Wang .J, Jiang X., Zhu Z., Dual-enhanced sparse decomposition for wind turbine gearbox fault diagnosis, IEEE Transactions on Instrumentation and Measurement, 68(2): 450–461, 2019, doi: 10.1109/TIM.2018.2851423.
  • 18. Daubechies I., Ten Lectures on Wavelets, Philadelphia, PA: SIAM, 1992.
  • 19. Figueiredo M.A.T., Bioucas-Dias J.M., Afonso M.V., Fast frame-based image deconvolution using variable splitting and constrained optimization, [in:] 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 109–112, 2009, doi: 10.1109/ SSP.2009.5278628.
  • 20. Antoni J., Fast computation of the kurtogram for the detection of transient faults, Mechanical Systems and Signal Processing, 21(1): 108–124, 2007, doi: 10.1016/j.ymssp. 2005.12.002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a1762be0-c3cf-4dc5-93f9-6cb3cf0d52ae
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