基于小波改进阈值去噪和HHT的滚动轴承故障诊断

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The vibration signal containing strong noise has great influence on results when HHT（Hilbert-Huang transformation） is applied to diagnose rolling bearing fault.To overcome this shortcoming,a signal analysis method based on the improved wavelet threshold de-noising method and HHT was proposed here.The rolling bearing fault signals were pretreated by using the improved wavelet threshold method,and then the IMFs（intrinsic mode functions） of the signals denoised were obtained with EMD（empirical mode decomposition）.To extract fault characteristic frequencies and judge fault types,the IMFs containing fault information were chosen to analyze the corresponding marginal spectrum.The results of simulations and tests verified the effectiveness of the proposed method.利用Hilbert-Huang变换（Hilbert-HuangTransformation,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（EmpiricalModeDecomposition,简称EMD）,接着选取含有故障信息的本征模函数（IntrinsicModeFunction,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。