基于双树复小波和奇异差分谱的滚动轴承故障诊断研究

针对滚动轴承故障振动信号中包含强烈噪声，很难提取故障特征频率的情况，提出了基于双树复小波和奇异差分谱的故障诊断方法。首先利用双树复小波将非平稳振动信号分解为几个不同频段的分量；然后对包含故障特征的分量构建Hankel矩阵并进行奇异值分解，求奇异值差分谱曲线，根据奇异值差分谱最大突变点来确定奇异值个数进行重构；最后再求希尔伯特包络谱，便能准确地得到故障频率。实验结果和工程应用表明，该方法可以有效地提取轴承故障的故障信息，提取出了故障特征，验证了方法的可行性和有效性。

Fault Diagnosis Based on Dual-tree Complex Wavelet Transform and Singular Value Difference Spectrum

Aiming at the strong background noise involved in the signals of roller bearing and the difficulty to obtain fault frequencies in practice, a new fault diagnosis method is proposed based on dual-tree complex wavelet transform and singular value difference spectrum. Firstly, original fault signals are decomposed into several different frequency band components through DT-CWT; Secondly, Hankel matrix is constructed by the component which contains the fault information, and the singular value difference spectrum can be obtained after SVD. Then the maximum catastrophe point is used to identify the number of singular value reconstruction component. Finally, the fault frequency can be identified accurately by Hilbert envelope spectrum. The results of the experiments and engineering application show that the fault feature of roller bearing can be separated effectively and the fault feature were extracted, the feasibility and effectiveness of the method were verified.