Wind turbines revolve in difficult operating conditions due to stochastic loads and produce massive vibration signals, which cause obstacles in detecting potential fault information. To overcome this, an adaptive fault detection approach is presented in this paper on the basis of parameterless empirical wavelet transform (PEWT) and the margin factor. PEWT can decompose the vibration signal into a series of empirical modes (EMs) through splitting its Fourier spectrum, using the scale space method and adaptively constructing an orthogonal wavelet filter bank. The margin factor is utilized as a key metric for automatically selecting the EM which is sensitive to the potential faults. The method presented in this paper will improve the efficiency and accuracy of fault information for the condition monitoring of wind turbines.
AntoniJ (2007a) Cyclic spectral analysis of rolling-element bearing signals: facts and fictions. Journal of Sound and Vibration304(3–5): 497–529.
2.
AntoniJ (2007b) Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing21(1): 108–124.
3.
CaoHRFanFZhouKet al.(2016) Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement82: 439–449.
4.
ChenJLLiZPPanJet al.(2016a) Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing70–71: 1–35.
5.
ChenJLPanJLiZPet al.(2016b) Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals. Renewable Energy89(1): 80–92.
6.
DaubechiesI (1992) Ten Lectures on Wavelets, Philadelphia, PA: Society for Industrial and Applied Mathematics.
7.
DekysVKalmanPHanakP (2017) Determination of vibration sources by using STFT. Procedia Engineering177: 496–501.
8.
GillesJ (2013) Empirical wavelet transform. IEEE Transactions on Signal Processing61(16): 3999–4010.
9.
GillesJHealK (2014) A parameterless scale-space approach to find meaningful modes in histograms – application to image and spectrum segmentation. International Journal of Wavelets, Multiresolution and Information Processing12(6): 1–17.
10.
GuoTDengZ (2017) An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing. Applied Acoustics127(1): 46–62.
11.
HuangNEShenZLongSR (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences454(1971): 903–995.
12.
KedadoucheMLiuZViet-HungV (2016a) A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis. Measurement90(3): 292–308.
13.
KedadoucheMThomasMTahanA (2016b) A comparative study between empirical wavelet transforms and empirical mode decomposition methods: Application to bearing defect diagnosis. Mechanical Systems and Signal Processing81: 88–107.
14.
KhakipourMHSafaviAASetoodehP (2017) Bearing fault diagnosis with morphological gradient wavelet. Journal of the Franklin Institute354(6): 2465–2476.
15.
LeiYGLinJHeZJet al.(2011) Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing25(5): 1738–1749.
16.
LindebergT (1994) Scale-Space Theory in Computer Vision, Boston, MA: Kluwer Academic.
17.
LiuHLiLMaJ (2016) Rolling bearing fault diagnosis based on STFT-deep learning and sound signals. Shock and Vibration2016: 1–12. Available at: https://www.hindawi.com/journals/sv/2016/6127479/ (accessed).
18.
RudemoM (1982) Empirical choice of histograms and kernel density estimators. Scandinavian Journal of Statistics9(2): 65–78.
19.
SheenYT (2004) A complex filter for vibration signal demodulation in bearing defect diagnosis. Journal of Sound and Vibration276(1–2): 105–119.
20.
SinghDSZhaoQ (2016) Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines. Mechanical Systems and Signal Processing81: 202–218.
21.
VDI 3834 (2009) (Germany, Verein Deutscher Ingenieure (VDI)) Measurement and evaluation of the mechanical vibration of wind energy turbines and their components onshore wind energy turbines with gears, p.16. Available at http://www.freestd.us/soft4/3990041.htm.
22.
WangBLiuYLiaoY (2015a) Sensitivity analysis on time domain characteristic index for fault signals of rolling bearings. Bearing10(1): 45–48. [In Chinese.].
23.
WangCGanMZhuC (2015b) Non-negative EMD manifold for feature extraction in machinery fault diagnosis. Measurement70: 188–202.
24.
WuZHHuangNE (2009) Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis1(1): 1–41.
YanRGaoRXChenX (2014) Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing96(1): 1–15.
27.
YuKLinTRTanJW (2017) A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath–Geva clustering. Applied Acoustics121(1): 33–45.
28.
ZhangFLiuYChenCet al.(2014) Fault diagnosis of rotating machinery based on kernel density estimation and Kullback–Leibler divergence. Journal of Mechanical Science and Technology28(11): 4441–4454.
29.
ŽvokeljMZupanSPrebilI (2016) EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. Journal of Sound and Vibration370: 394–423.
