双树复小波包和ICA用于滚动轴承复合故障诊断

Compound Fault Diagnosis of Rolling Bearing Based on Dual-tree Complex Wavelet Packet Transform and ICA

针对滚动轴承复合故障信号特征难以分离的问题,提出将双树复小波包变换和独立分量分析(independent component analysis,简称ICA)结合的方法应用到滚动轴承复合故障诊断中。首先,利用双树复小波包变换将复杂的、非平稳的复合故障信号分解为若干不同频带的分量;其次,引入ICA对各个分量所组成的混合信号进行盲源分离,从而尽可能消除频率混叠;最后,对从混合信号中分离出来的独立信号分量进行希尔伯特解调,即可实现对复合故障特征信息的分离和故障识别。试验结果表明,该方法可以有效地分离和提取轴承复合故障的特征频率,验证了方法的可行性和有效性。

A new fault diagnosis method is proposed based on the dual-tree complex wavelet packet transform(DT-CWPT)and independent component analysis(ICA),aiming at separating fault information from the compound rolling bearing fault signal.First,the non-stationary and complex signal of the compound fault is decomposed into several different frequency band components through dual-tree complex wavelet packet decomposition.Then,ICA is used to separate the mixed signal consisting of each component to eliminate the frequency aliasing as much as possible.Finally,independent signal components separated from the mixed signal are processed by Hilbert demodulation.The results show that the fault feature of rolling bearing can be effectively separated and extracted,and the method′s feasibility and effectiveness are verified.