基于改进EMD和谱峭度法滚动轴承故障特征提取  

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

作　　者：张志刚[1] 石晓辉[1] 施全[1] 汤宝平[2] 

机构地区：[1]重庆理工大学汽车零部件制造及检测技术教育部重点实验室重庆,400054 [2]重庆大学机械传动国家重点实验室重庆,400044

出　　处：《振动．测试与诊断》2013年第3期478-482,529-530,共5页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(50875272);国家高技术研究发展计划(“八六三”计划)资助项目(2009AA04Z411)

摘　　要：针对滚动轴承故障信号的强背景噪声特点,提出一种基于改进经验模态分解(empirical mode decomposi-tion,简称EMD)与谱峭度法的滚动轴承故障特征提取方法。首先,利用EMD方法对原故障信号进行分解,得到若干平稳固有模态分量(intrinsic mode function,简称IMF);然后,采用灰色关联度与互信息相结合方法剔除传统EMD分解结果中存在的虚假分量;最后,运用谱峭度法和包络解调方法对真实IMF分量进行分析,提取故障特征频率。通过对实际滚动轴承故障信号的应用表明,该方法可有效地提取滚动轴承故障特征,且能够取得比传统包络解调分析更好的效果。Considering the strong background noise of rolling element bearing fault signal,a rolling element bearing fault feature extraction method is proposed based on improved EMD(empirical mode decomposition) and spectral kurtosis method.Original fault signal is decomposed by EMD to contain a finite number of stationary intrinsic mode functions(IMFs).Then grey incidence and mutual information are used together to remove pseudo-components in the traditional EMD results.Finally,real IMF component is analyzed by the spectral kurtosis and the envelope demodulation 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 the envelope demodulation in fault feature extraction.

关 键 词：滚动轴承 故障特征 提取 改进EMD 谱峭度 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置] TN911[自动化与计算机技术—控制科学与工程]