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中文摘要：
滚动轴承的故障信号采集中往往含有大量的噪声信号。对采集信号进行小波包降噪后,利用经验模态分解（empirical mode decomposition,EMD）得到若干个固有模态函数（intrinsic mode function,IMF）。计算各个IMF与去噪后信号的相关系数以此确定哪几个IMF是待分析信号的有效集,根据有效集中IMF的突变程度来选择不同消失矩的db系小波进行小波降噪。对IMF进行边际谱分析来判断滚动轴承哪个部位发生故障。该方法有效地去除了混杂在故障信号中的噪声,提高了信噪比,准确地判断出滚动轴承发生故障的部位。
英文摘要：
The collected fault signals of roller bearings usually contain a lot of noise disturbance. In this paper, through the wavelet packet noise reduction for the signals using the empirical mode decomposition （EMD） method, several intrinsic mode functions （IMF） were obtained. The correlation coefficients of the IMF and the denoised signalsowere computed so as to determine which IMFs were in the available set of the signals to be analyzed. Then, according to the mutation extent of the IMFs, Daubechies wavelets of different vanishing moments for wavelet denoising were selected. Finally, through the marginal spectrum analysis of IMF, the fault positions of the bearings were determined. Using this method, the noise disturbance in the signals can be removed effectively, the signal-to-noise ratio can be raised, and the fault positions of the roller bearings can be determined precisely.
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