基于改进EMD和滑动峰态算法的滚动轴承故障特征提取

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振动与冲击 ›› 2012, Vol. 31 ›› Issue (22) : 80-83.

论文

基于改进EMD和滑动峰态算法的滚动轴承故障特征提取

张志刚1 石晓辉1 陈哲明1 汤宝平2

作者信息 +

Fault Feature Extraction of Rolling Element Bearing Based on Improved EMD and Sliding Kurtosis Algorithm

ZHANG Zhigang1 SHI Xiaohui1 CHEN Zheming1 TANG Baoping2

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文章历史 +

摘要

针对滚动轴承故障特征往往被强背景噪声淹没的特点，提出一种基于改进经验模态分解(Empirical Mode Decomposition,EMD)与滑动峰态算法的滚动轴承故障特征提取方法。首先利用EMD方法分解原故障信号得到一组平稳固有模态分量(Intrinsic Mode Function,IMF)。然后采用互信息和广义相关系数筛选法消除传统EMD分解结果中虚假分量，并运用滑动峰态算法对真实IMF分量处理得到滑动峰态时间序列。最后计算滑动峰态序列频谱提取故障特征频率。滚动轴承的实例研究结果表明：该方法能够有效提取滚动轴承故障特征，可以取得比直接滑动峰态算法和传统包络解调分析更好的效果。

Abstract

Considering the characteristics of strong noise of rolling element bearing fault signal, a rolling element bearing fault feature extraction method was proposed based on improved EMD and sliding kurtosis algorithm. Original fault signal was decomposed by EMD to get a finite number of stationary intrinsic mode functions(IMFs). Then mutual information and general correlation coefficient were together used to get rid of pseudo-components in the traditional EMD results, and Real IMF component was processed by sliding kurtosis algorithm to obtain a sliding kurtosis time series. Finally, frequency spectrum of the time series was calculated by Fourier transform to extract the fault feature frequency. Experimental results show that the method can effectively extract the fault feature of rolling element bearing, and is more effective than direct sliding kurtosis algorithm and traditional envelope demodulation in fault feature extraction.

导出引用

张志刚石晓辉陈哲明汤宝平. 基于改进EMD和滑动峰态算法的滚动轴承故障特征提取[J]. 振动与冲击, 2012, 31(22): 80-83

ZHANG Zhigang SHI Xiaohui CHEN Zheming TANG Baoping. Fault Feature Extraction of Rolling Element Bearing Based on Improved EMD and Sliding Kurtosis Algorithm[J]. Journal of Vibration and Shock, 2012, 31(22): 80-83