声测法和经验模态分解在轴承故障诊断中的应用

提出了一种基于声测法、经验模态分解(EMD)和包络谱分析的轴承故障诊断新方法。声测法是轴承故障诊断最有效方法之一,但在获得的声测信号中含有大量噪声,严重影响了信号处理的结果。采用EMD可以有效地实现对信号的处理,大大地提高信噪比。EMD是把时间序列信号,分解成不同特征时间尺度的固有模态函数(IMF),具有自适应的分析能力,通过选取表征轴承故障的IMF分量进行包络谱分析,可提取轴承故障信号的特征。实验结果表明该方法能有效地诊断轴承故障。

Application Acoustic Emission and Empirical Mode Decomposition to Faults Diagnosis of Bearing

A novel method to fault diagnosis of bearing based on acoustic emission,empirical mode decomposition(EMD)and envelope spectrum is presented.EMD method is self-adaptive to non-stationary and non-linear data processing.Firstly,the experimental data is decomposed into a finite and often small number of intrinsic mode functions,using the empirical mode decomposition.Then the envelope spectrum is applied to the selected intrinsic mode function which stands for the bearing faults.The basic principle is introduced in detail.The EMD is applied in the research of the faults diagnosis of the bearing.The experimental results show that this method based on acoustic emission,EMD and envelope spectrum can effectively diagnosis the faults of bearing.