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Noise suppression and characteristic frequency extraction of wind turbine vibration based on EMD correlation denoising
Pages: 73-80
Year: Issue:  1
Journal: Electric Machines and Control

Keyword:  wind turbinecondition monitoringnoise suppressionempirical mode decompositionwave-let package transform;
Abstract: frequency extraction method combining empirical mode decomposition ( EMD) , correlation analysis with wavelet package transform ( WPT) were studied. This method, firstly, decomposes the vibration signals into a series of intrinsic mode functions ( IMFs) which represent different frequencies by using EMD. Then, a fault characteristic signal was restructured by accumulating the selected IMFs which characterize the fault characteristic frequencies. Secondly, the characteristic signals were analyzed by using the meth-od of autocorrelation analysis to eliminate the interference of the noises. Finally, the characteristic fre-quency is extracted by using the WPT from de-noising restructured vibration signals. WPT, EMD correla-tion denoising-WPT and wavelet hard thresholding-WPT were used to analyze the actual and simulating wind turbines bearing fault vibration signals to verify the effectiveness of the proposed method. The results of comparing with the different characteristic frequency extraction methods show that the presented charac-teristic frequency extraction method based on EMD correlation denoising-WPT can effectively depress the white noise and short-term disturbance noise, and extract early weak fault feature.
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