Frontiers of Mechanical Engineering >
Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum
Received date: 18 Aug 2016
Accepted date: 18 Nov 2016
Published date: 04 Aug 2017
Copyright
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.
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
1 |
Amirat Y, Benbouzid M E H, Al-Ahmar E,
|
2 |
Hameed Z, Hong Y S, Cho Y M,
|
3 |
Feng Z, Liang M, Zhang Y ,
|
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
|
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
|
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
|
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
|
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
|
9 |
Lei Y, Lin J, Zuo M J ,
|
10 |
Li C, Sanchez V, Zurita G ,
|
11 |
Cong F, Zhong W, Tong S ,
|
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
|
13 |
Antoni J. The spectral kurtosis: A useful tool for characterizing non-stationary signals. Mechanical Systems and Signal Processing, 2006, 20(2): 282–307
|
14 |
Antoni J. Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 2007, 21(1): 108–124
|
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
|
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
|
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
|
18 |
Lei Y, Lin J, He Z ,
|
19 |
Yan R, Gao R, Chen X . Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 2014, 96: 1–15
|
20 |
Chen J, Li Z, Pan J ,
|
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
|
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
|
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 ,
|
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 ,
|
/
〈 | 〉 |