Title :
Fault Recognition of Wind Turbine Using EMD Analysis and FFT Classification
Author :
Lin, Deng-Fa ; Chen, Po-Hung
Author_Institution :
Dept. of Electr. Eng., St. John´´s Univ., Taipei, Taiwan
fDate :
July 31 2012-Aug. 2 2012
Abstract :
This paper employs empirical mode decomposition (EMD) and fast Fourier transform (FFT) to analyze the oil-leakage fault signal of the gearbox of wind turbines. K-nearest neighbors (KNN) is used on automatic fault recognition. First, both normal and faulty oil-leakage gearboxes are considered. Second, EMD is applied on analyzing the intrinsic mode function (IMF) of the current signals, and FFT is used to get the IMF spectrum. Finally, the features of the spectrum are extracted, and KNN is used on fault recognition of wind turbine gearboxes. The result indicates that it can effectively recognize the oil-leakage fault of gearboxes.
Keywords :
fast Fourier transforms; fault diagnosis; feature extraction; leak detection; oils; pattern classification; singular value decomposition; wind turbines; EMD; FFT classification; IMF; K-nearest neighbors; KNN; automatic fault recognition; empirical mode decomposition; fast Fourier transform; intrinsic mode function; oil leakage fault signal analysis; spectrum feature extraction; wind turbine gearbox; Current measurement; Fast Fourier transforms; Feature extraction; Generators; Magnetics; Vibrations; Wind turbines; Empirical Mode Decomposition; Fast Fourier Transform; Intrinsic Mode Function; K-nearest Neighbors;
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2012 Third International Conference on
Conference_Location :
GuiLin
Print_ISBN :
978-1-4673-2217-1
DOI :
10.1109/ICDMA.2012.99