基于双树复小波和奇异差分谱的齿轮故障诊断研究

针对齿轮故障振动信号的非平稳特性和包含强烈噪声,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。首先将非平稳的故障振动信号通过双树复小波分解为几个不同频段的分量;由于噪声的影响,从各个分量的频谱中难以准确地得到故障频率。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,确定奇异值个数进行SVD重构降噪,由此实现对故障特征信息的提取。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齿轮的故障特征信息,验证了方法的可行性和有效性。

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

Aiming at the strong background noise involved in the non-stationary signals of fault gear 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 dual-tree complex wavelet decomposition; but it is often difficult to obtain fault frequencies exactly from components because of strong background noise. Secondly, Hankel matrix is constructed by the component which contains the fault information, and the singular value difference spectrum can be obtained after singular value decomposition. Then the number of singular value will be obtained to realize signal de-noising by the SVD reconstruction .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 gear can be separated effectively and the fault feature were extracted, the feasibility and effectiveness of the method were verified.