| Summary: | This paper presents a novel solution method based on measurement and analysis of current signals for gearbox fault recognition of wind turbine. A gearbox with typical oil-leakage fault is purposely made. The oil-leakage gearbox and a normal gearbox are used as experimental models to measure and analyze the current signals of generator. This work employs wavelet transform (WT), empirical mode decomposition (EMD) and fast Fourier transform (FFT) to analyze the current signals for both the oil-leakage and the normal gearboxes. K-nearest neighbors (KNN) is used on automatic fault recognition. First, the normal gearbox and the oilleakage gearbox are separately applied to practical power platform experiments. Second, empirical mode decomposition is applied on analyzing the intrinsic mode function (IMF) of the current signals, and fast Fourier transform is used to get the intrinsic mode function spectrum. Finally, the features of the spectrum are extracted, and K-nearest neighbors is used on gearbox fault recognition of wind turbine. Experimental results indicate that the proposed solution method can effectively recognize the oilleakage fault of gearboxes.
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