基于小波包和EMD的滚动轴承故障诊断  

Fault Diagnosis for Rolling Bearing Based on Wavelet Packet and EMD

作　　者：李泽豪[1] 顾海明[1] 张亿雄[1] 

机构地区：[1]南京工业大学,南京210009

出　　处：《煤矿机械》2010年第6期243-245,共3页Coal Mine Machinery

摘　　要：针对滚动轴承故障振动信号的非平稳特征,提出了一种基于小波包和经验模态分解(Empirical Mode Decomposition,简称EMD)的滚动轴承故障诊断方法。该方法用小波包对振动信号进行预处理,用Hilbert变换求重构信号的包络,采用EMD方法将包络信号分解为若干个IMF分量,让故障信息得到凸显,然后根据某个分量的频谱,判断滚动轴承的故障类型。实验结果表明,比传统的时频分析方法,该方法能够更有效地提取轴承故障特征,诊断轴承故障。According to the non-stationary characteristics of vibration signals from fault roller bearing, a fault diagnosis approach for roller bearings based on wavelet packet and EMD （Empirical Mode Decomposition）method is proposed. Provide the wavelet packet analysis as vibration signal pretreatment means. Gain the envelope of reconstructing signal by using Hilbert transform, and then using the EMD method to decompose the envelope signal into many of intrinsic mode function （IMF） components, so as to highlights the failure information. The frequency spectrum of some IMF component is used to identify the failure pattern of a rolling bearing. Practical examples show that this method can detect rolling bearing failure more effectively comparing with the traditional analysis method.

关 键 词：小波包 经验模态分解 滚动轴承 故障诊断 

分 类 号：TB123[理学—工程力学] TH133.33[理学—力学]