基于小波改进阈值去噪和HHT的滚动轴承故障诊断

利用Hilbert-Huang变换（Hilbert-Huang Transformation，简称HHT）对滚动轴承进行故障诊断时，发现振动信号中包含的噪声对诊断结果影响较大。为克服此不足，提出了一种小波改进阈值法与HHT相结合的信号分析方法。该方法首先应用小波改进阈值方法对滚动轴承故障信号进行预处理，然后对去噪后的信号进行经验模态分解（Empirical Mode Decomposition，简称EMD），接着选取含有故障信息的本征模函数（Intrinsic Mode Function，简称IMF）分量进行边际谱分析，从而提取出故障特征频率，并判断故障类型。仿真和实验结果验证了该方法的有效性。

Rolling bearing fault diagnosis based on improved wavelet threshold de-noising and HHT

The vibration signal, containing strong noise, has great influence on results when HHT (Hilbert-Huang Transformation) is applied to the rolling bearing fault diagnosis. To overcome this limitation, a signal analysis method based on the improved wavelet threshold de-noising and HHT is proposed in this paper. Pretreat the rolling bearing fault signal by using the improved wavelet threshold method, and then the EMD (Empirical Mode Decomposition) is performed on the de-noising signal. To exact the fault characteristic frequency and judge fault types, the IMFs (Intrinsic Mode Functions) relating to fault information are chosen to analyze the marginal spectrum. The results of simulation and experiment are presented to verify the theory analysis.