针对 Morlet 小波变换结果中的特征提取问题，对连续小波变换得到的小波系数矩阵进行奇异值分解( SVD) ，分析了所获得的奇异值与 Morlet 小波变换结果中的特征信号以及噪声的对应关系．基于这种关系，通过选择合适的奇异值进行重构，清晰地提取到 Morlet小波分解结果中的有效特征信息; 进一步计算得到频率 －能量谱，根据峰值位置能够提取冲击特征．将该方法应用于轴承振动信号的故障特征提取，并与其他方法进行了比较．结果表明，文中方法所获得的故障波形非常清晰，在低信噪比时具有较好的故障特征提取效果．

Aiming at the feature extraction of Morlet wavelet transform results,the wavelet coefficient matrixobtained by the continuous Morlet wavelet transform is decomposed by singular value decomposition (SVD).Therelationship among the singular value,the feature signal and the noise in the Morlet wavelet transform results isanalyzed.Based on this relationship,the effective feature information of wavelet transform results can be clearlyextracted by selecting appropriate singular values for SVD reconstruction.Further calculation is carried out to obtainthe frequency- energy spectrum,and the shock feature can be extracted according to the peak position of this spec-trum.Finally,the proposed method is applied to the fault feature extraction of bearing vibration signals and is com-pared with other methods.The results show that the proposed method can extarct the distinct fault waveforms andachieve a very good effect on fault feature extraction at a low signal- to- noise ratio(SNR).