基于包络谱分析的滚动轴承故障诊断方法研究

发布时间：2018-01-15 16:03

本文关键词：基于包络谱分析的滚动轴承故障诊断方法研究　出处：《煤矿机械》2017年02期 　论文类型：期刊论文

【摘要】：分别用小波分解、小波包分解和EMD分解处理滚动轴承故障数据,并结合Hilbert变换进行包络谱分析实现滚动轴承故障诊断。对滚动轴承故障数据进行小波阈值降噪。小波阈值降噪后分别进行小波分解、小波包分解和EMD分解。分别求出小波分解、小波包分解和EMD分解后各个频带的能量谱。再根据能量谱确定故障频带范围并对其进行信号重构。采用Hilbert变换对重构信号进行包络谱分析实现滚动轴承故障诊断。通过对滚动轴承内圈故障信号的分析验证了小波分解、小波包分解和EMD分解结合Hilbert变换进行包络谱分析的滚动轴承故障诊断方法的有效性。
[Abstract]:Respectively, using wavelet decomposition, wavelet packet decomposition and EMD decomposition of rolling bearing fault data, and realize the envelope spectrum analysis of fault diagnosis of rolling bearing based on Hilbert transform. The rolling bearing fault data of wavelet threshold denoising. Wavelet threshold denoising after wavelet decomposition, wavelet packet decomposition and EMD decomposition were obtained. Wavelet decomposition, wavelet packet decomposition and EMD of each frequency band energy spectrum decomposition. According to the energy spectrum to determine the fault frequency range and the signal reconstruction. Using Hilbert transform to reconstruct signal envelope spectrum analysis realization of rolling bearing fault diagnosis. Through the analysis of the inner ring of the rolling bearing fault signal to verify the wavelet decomposition, wavelet packet decomposition and EMD decomposition based on Hilbert transform is effective method of fault diagnosis of rolling bearing analysis of the envelope spectrum.

【作者单位】：上海电力学院计算机科学与技术学院;
【分类号】：TH133.33
【正文快照】： 0 引言典型的滚动轴承故障包括外圈故障、内圈故障和滚动体故障,不同的滚动轴承故障对应着不同的故障频率。所以,可以通过检测不同的故障频率进行滚动轴承故障诊断。检测不同的故障频率的方法主要包括小波分解、小波包分解和EMD分解等。小波分解仅对信号的低频部分进行分解

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