Based on the original signal that contains Gaussian noise and white noise, performance indexs of η and the signal to noise ratios (SNR) are defined. Three families of mother wavelets (Symlets , Daubechies and Coiflet) , four threshold selection rules ( Rigrsure, Sqtwolog , Heursure and Manimaxi) , and three threshold rescaling methods (One , Sln and Mln) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. The proposed approach is applied into the fault feature extraction of reciprocating compressor. The result shows that the best denoising performance is the combinations of "Daubechies4" wavelets , "Heursure" threshold selection rule , and "Sln" threshold rescaling method. It's not only threshold denoising, but also keep the original feature information for signal.%在仿真含高斯噪声和白噪声原始信号的基础上,使用了信噪比(SNR)和维持率(KP)性能指标,评估了采用3种小波族系(Symlets,Daubechies及Coiflet)、4种阈值选取方法(Rigrsure,Sqtwolog,Heursure和Manimaxi)和3种阈值重调方法(One,Sln,Mln)组合参数的去噪能力,并将最佳参数组合的小波降噪方法应用于往复压缩机故障特征提取.结果表明:采用Db4小波、Heursure阈值选取方法及Sln阈值重调方法,可以得到最优的去噪性能,不仅能够有效降低噪声往复压缩机信号中的噪声干扰,还最大限度的保持了原故障信号的特征.
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