Username   Password       Forgot your password?  Forgot your username? 

 

Fault Diagnosis of Wind Turbine Blades based on Wavelet Theory and Neural Network

Volume 15, Number 7, July 2019, pp. 1895-1904
DOI: 10.23940/ijpe.19.07.p16.18951904

Junxi Bia,b, Chenglong Zhengb, Hongzhong Huangc, Xiaojuan Songb, and Jinfeng Lid

aAviation College, Inner Mongolia University of Technology, Hohhot, 010051, China
bCollege of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China
cInstitute of Reliability Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
dInner Mongolia Institute of Metrology Testing and Research, Hohhot, 010020, China

(Submitted on April 13, 2019; Revised on May 25, 2019; Accepted on June 25, 2019)

Abstract:

With the development of the wind turbine industry, the reliability requirements of wind turbine blades are continuously increasing. In this paper, static load fatigue experiments are carried out on wind turbine blades, and the collected fault data of blades are extracted using the wavelet transform method. Wavelet theory is applied to remove the noise of the data and eliminate the interference of noise on the fault diagnosis of wind turbine blades. Then, the wavelet decomposition method is used to decompose high frequency signals and low frequency signals. The faulty low frequency signals are extracted and analyzed in the time domain, and a fault diagnosis method of wind turbine blade is established. The data of different vibration frequencies of wind turbine blades are collected by the acquisition system, and the data are imported into the neural network. The neural network is used to process the data and identify the states of wind turbine blades. The neural network proves that the wavelet transform method has reliable fault diagnosis ability in time domain analysis.

 

References: 25

  1. H. Badihi, Y. M. Zhang, and S. Rakheja, “Model-based Fault-Tolerant Pitch Control of an Offshore Wind Turbine,” IFAC-Papers on Line, Vol. 51, No. 18, pp. 221-226, 2018
  2. D. H. Wu and Y. J. Zhai, “Fault Diagnosis of Wind Turbine Pitch System based on System Identification Algorithms,” Information and Control, Vol. 10, No. 5, pp. 563-574, 2016
  3. Z. W. Gao and S. W. Sheng, “Real-Time Monitoring, Prognosis, and Resilient Control for Wind Turbine Systems,” Renewable Energy, Vol. 116, No. 1, pp. 1-4, 2018
  4. X. X. Zhang and Z. X. Liu, “Application of Resonance Demodulation and Wavelet Noise Reduction in Motor Fault Diagnosis,” Electric Machines and Control, Vol. 2, No. 6, pp. 66-70, 2013
  5. Z. H. Zhao, S. P. Yang, and Y. Q. Liu, “Application of Multi-Wavelet Coefficient Feature Extraction Method in Fault Diagnosis,” Journal of Vibration, Measurement and Diagnosis, Vol. 12, No. 2, pp. 276-282, 2015
  6. H. Z. Huang, X. D. Zhang, and D. B. Meng, “Multidisciplinary Design Optimization with Discrete and Continuous Variables of Various Uncertainties,” International Journal of Computational Intelligence Systems, Vol. 5, No. 1, pp. 93-110, 2012
  7. X. Yin, X. Y. Zhang, and X. Q. Chang, “Research on Early Fault Diagnosis Method of Wind Turbine based on AR-Hankel Matrix,” Renewable Energy, Vol. 32, No. 1, pp. 80-85, 2016
  8. T. Regan, C. Beale, and M. Inalpolat, “Wind Turbine Blade Damage Detection using Supervised Machine Learning Algorithms,” Journal of Vibration and Acoustics, Vol. 139, No. 6, pp. 10-24, 2017
  9. Y. G. Xu, Z. P. Meng, and M. Lu, “Application of Dual-Tree Complex Wavelet and Singular Difference Spectrum in Fault Diagnosis of Rolling Bearing,” Journal of Vibration Engineering, Vol. 7, No. 6, pp. 965-973, 2013
  10. L. J. Xu and L. Xu, “Test and Research on Static Load Test of 100 Kw Wind Turbine Blade,” Process Automation Instrumentation, Vol. 9, No. 7, pp. 37-42, 2016
  11. M. A. Eder, F. Belloni, and A. Tesauro, “A Multi-Frequency Fatigue Testing Method for Wind Turbine Rotor Blades,” Journal of Sound and Vibration, Vol. 388, pp. 123-140, 2017
  12. S. X. Liu, L. P. Li, and T. Yu, “Diagnosis Technology of Wind Turbine Blade Icing State based on Vibration Detection,” Proceedings of the CSEE, Vol. 9, No. 32, pp. 88-95, 2013
  13. J. X. Bi, C. L. Zheng, and H. Z. Huang, “Load Analysis and Calculation Optimization of Horizontal Axis Wind Turbine Blades,” International Journal of Performability Engineering, Vol. 14, No. 12, pp. 3098-3108, 2018
  14. M. Gong and L. P. Li, “Summary of Research on Application of Acoustic Emission Technology in Wind Turbine Blade Fault Detection,” Solar Energy, Vol. 1, No. 1, pp. 57-62, 2018
  15. X. F. Chen, J. M. Li, and H. Cheng, “Research and Application of Condition Monitoring and Fault Diagnosis Technology in Wind Turbines,” Journal of Mechanical Engineering, Vol. 47, No. 9, pp. 45-52, 2011
  16. J. Z. Yan, L. Wang, and J. Zhou, “Development of on-Line Monitoring Terminal for Blade Cracking Defects of Wind Turbine,” Electrical Automation, Vol. 3, No. 9, pp. 116-120, 2015
  17. J. H. Zhang, J. Xiong, and M. F. Ren, “Filter-based Fault Diagnosis of Wind Energy Conversion Systems Subject to Sensor Faults,” Journal of Dynamic Systems, Measurement, and Control, Vol. 138, No. 6, pp. 8-18, 2015
  18. W. X. Yang, “Condition Monitoring the Drive Train of a Direct Drive Permanent Magnet Wind Turbine using Generator Electrical Signals,” Journal of Vibration and Acoustics, Vol. 136, No. 2, pp. 21-28, 2015
  19. B. J. Qiao, X. F. Chen, and X. J. Luo, “A Novel Method for Force Identification based on the Discrete Cosine Transform,” Journal of Vibration and Acoustics, Vol. 137, No. 5, pp. 51-64, 2015
  20. X. Wang, X. Pang, and Y. X. Wang, “Optimized VMD-Wavelet Packet Threshold Denoising based on Cross-Correlation Analysis,” International Journal of Performability Engineering, Vol. 14, No. 9, pp. 2239-2247, 2018
  21. L. Li, X. L. Zhang, and Y. H. Li, “Analysis of Coupled Vibration Characteristics of Wind Turbine Blade based on Green's Functions,” Acta Mechanica Solida Sinica, Vol. 29, No. 6, pp. 620-630, 2015
  22. B. Basu and A. Staino, “Control of a Linear Time-Varying System with a Forward Riccati Formulation in Wavelet Domain,” Journal of Dynamic Systems, Measurement, and Control, Vol. 138, No. 10, pp. 72-78, 2016
  23. M. Du, J. Yi, and J. B. Guo, “Application of Neural Network Technology in SCADA Data Analysis of Wind Turbines,” Power System Technology, No. 7, pp. 2200-2205, 2018
  24. Y. S. He, Y. Huang, and Z. M. Xu, “Fault Recognition of Motor Bearing based on Wavelet Singular Entropy and SOFM Neural Network,” Journal of Vibration and Shock, Vol. 35, No. 10, pp. 217-223, 2017
  25. P. L. Zhang, H. Chang, and J. Yang, “Application of BP Neural Network in AE Source Location of Cracks in Wind Turret Tube,” China Measurement and Test, Vol. 12, No. 9, pp. 106-111, 2017

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

 
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com