基于双树复小波包和AR谱的滚动轴承复合故障诊断方法

Compound Fault Diagnosis Based on Dual-tree Complex Wavelet Packet Transform and AR Spectrum for Rolling Bearings

摘要: 针对滚动轴承复合故障信号中故障特征难以分离的问题,提出了基于双树复小波包和自回归(autoregressive,AR)谱的故障诊断方法.首先,利用双树复小波包变换将复杂的、非平稳的复合故障振动信号分解为若干个不同频带的分量;然后,对包含故障特征的分量进行希尔伯特包络;最后,对包络信号求其AR功率谱,由此实现对复合故障特征信息的分离和故障识别.实验结果表明:该方法可有效地分离轴承复合故障的特征频率,验证了该方法的可行性和有效性.

Abstract: Aimed at separating fault information from compound rolling bearing fault signal,a fault diagnosis method was proposed based on dual-tree complex wavelet packet transform and auto-regressive(AR) spectrum. First,the non-stationary and complex signal of compound fault was decomposed into several different frequency band components through dual-tree complex wavelet packet decomposition. Second,Hilbert envelope was formed from the component that contains the fault information. Finally,the power spectrum was obtained by AR spectrum. Thus,the information of fault feature was separated and identified. Experiments results show that the fault feature of rolling bearing can be separated effectively, and the feasibility and effectiveness of the method are verified.