RESEARCH ARTICLE

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

  • Yun KONG ,
  • Tianyang WANG ,
  • Zheng LI ,
  • Fulei CHU
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  • Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China

Received date: 18 Aug 2016

Accepted date: 18 Nov 2016

Published date: 04 Aug 2017

Copyright

2017 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

Cite this article

Yun KONG , Tianyang WANG , Zheng LI , Fulei CHU . Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum[J]. Frontiers of Mechanical Engineering, 2017 , 12(3) : 406 -419 . DOI: 10.1007/s11465-017-0419-0

Acknowledgments

The authors gratefully appreciate all the reviewers and the editor for their valuable comments and advices about our manuscript. The authors gratefully acknowledge the support of this research work by the National Natural Science Foundation of China (Grant No. 51335006).
1
Amirat Y, Benbouzid  M E H, Al-Ahmar  E, A brief status on condition monitoring and fault diagnosis in wind energy conversion systems. Renewable & Sustainable Energy Reviews, 2009, 13(9): 2629–2636

DOI

2
Hameed Z, Hong  Y S, Cho  Y M, Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable & Sustainable Energy Reviews, 2009, 13(1): 1–39

DOI

3
Feng Z, Liang  M, Zhang Y , Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation. Renewable Energy, 2012, 47: 112–126

DOI

4
Younus A M D ,  Yang B S . Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Systems with Applications, 2012, 39(2): 2082–2091

DOI

5
Toutountzakis T, Tan  C K, Mba  D. Application of acoustic emission to seeded gear fault detection. NDT & E International, 2005, 38(1): 27–36

DOI

6
Ottewill J R, Orkisz  M. Condition monitoring of gearboxes using synchronously averaged electric motor signals. Mechanical Systems and Signal Processing, 2013, 38(2): 482–498

DOI

7
Li C, Liang  M. Extraction of oil debris signature using integral enhanced empirical mode decomposition and correlated reconstruction. Measurement Science & Technology, 2011, 22(8): 085701

DOI

8
Samuel P D, Pines  D J. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration, 2005, 282(1‒2): 475–508

DOI

9
Lei Y, Lin  J, Zuo M J , Condition monitoring and fault diagnosis of planetary gearboxes: A review. Measurement, 2014, 48: 292–305

DOI

10
Li C, Sanchez  V, Zurita G , Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time-frequency ridge enhancement. ISA Transactions, 2016, 60: 274–284

DOI

11
Cong F, Zhong  W, Tong S , Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis. Journal of Sound and Vibration, 2015, 344: 447–463

DOI

12
Ho D, Randall  R B. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical Systems and Signal Processing, 2000, 14(5): 763–788

DOI

13
Antoni J. The spectral kurtosis: A useful tool for characterizing non-stationary signals. Mechanical Systems and Signal Processing, 2006, 20(2): 282–307

DOI

14
Antoni J. Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 2007, 21(1): 108–124

DOI

15
Antoni J, Randall  R B. The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 2006, 20(2): 308–331

DOI

16
Wang Y, Liang  M. An adaptive SK technique and its application for fault detection of rolling element bearings. Mechanical Systems and Signal Processing, 2011, 25(5): 1750–1764

DOI

17
Barszcz T, Randall  R B. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mechanical Systems and Signal Processing, 2009, 23(4): 1352–1365

DOI

18
Lei Y, Lin  J, He Z , . Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2011, 25(5): 1738–1749

DOI

19
Yan R, Gao  R, Chen X . Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 2014, 96: 1–15

DOI

20
Chen J, Li  Z, Pan J , . Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 2016, 70 ‒ 71: 1–35

DOI

21
Lin J, Qu  L. Feature extraction based Morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration, 2000, 234(1): 135–148

DOI

22
Jiang Y, Tang  B, Liu W . Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD. Renewable Energy, 2011, 36(8): 2146–2153

DOI

23
Dwyer R F. Detection of non-Gaussian signal by frequency domain kurtosis estimation. In: Proceedings of the International Conference on Acoustic, Speech, and Signal Processing. Boston, 1983, 607–610

24
Wang Y, Xiang  J, Markert R , . Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 2016, 66–67: 679–698

DOI

25
Program for the fast kurtogram provided by J. Antoni. Retrieved from https://cn.mathworks.com/matlabcentral/fileexchange/48912-fast-kurtogram

26
Chu F, Peng  Z, Feng Z , . Modern Signal Processing Methods in Machinery Fault Diagnosis. Beijing: Science Press, 2009, 34–37 (in Chinese)

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