基于EMD和峭度的Hilbert包络解调在滚动轴承故障诊断中的应用分析

针对直接运用快速傅里叶变换（FFT）无法有效提取具有非线性非平稳特性的滚动轴承振动信号故障特征频率的问题,提出了一种基于经验模式分解和峭度指标的Hilbert包络解调方法.首先对滚动轴承的振动信号进行了经验模式分解（EMD）,得到了包含轴承故障特征信息的各阶本征模态函数（IMF）,再计算各阶IMF的峭度值,选取了峭度值较大的几阶IMF分量重构信号,并对重构信号进行了Hilbert包络解调分析,从而获得了滚动轴承的准确故障特征信息.分别对仿真模拟信号和实际滚动轴承发生内圈故障的振动信号进行了分析,清晰地得到了故障特征频率.研究结果表明,利用融合EMD、峭度系数和Hilbert包络解调的诊断方法能够快速、准确地提取滚动轴承的故障特征频率,从而可以对滚动轴承进行有效地故障诊断.

Analysis of rolling bearing fault diagnosis based on EMD and kurtosis Hilbert envelope demodulation

Aiming at solving the problem that fast fourier transformation （FFT） is hardly applied to extract the characteristics frequency of the bearing＇s signal,a fault diagnosis method based on empirical mode decomposition （EMD）,kurtosis and Hilbert demodulation were proposed.Firstly,EMD was used to decompose the vibration signal into the intrinsic mode function （IMF）.Then,some of the IMFs selected by the rule of kurtosis were used to recombine the new vibration signal.At last,the Hilbert envelope demodulation was adopted with the new signal to detect the fault information.Through analyzing the simulation signal and the inner vibration signal of fault rolling bearing respectively,the characteristics frequency could be clearly extracted.The results indicate that the proposed method is effective in extracting the bearings＇ fault information and could be used in rolling bearings fault diagnosis.