基于LMD能量熵的滚动轴承故障特征提取
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摘要
为了对滚动轴承运行状态进行有效的判断,利用局部均值分解(LMD)对滚动轴承振动信号进行分解,将复杂的多分量信号分解成多个单分量信号;针对分解后的单分量信号在各频域范围分布不均匀特点,利用LMD能量熵提取出滚动轴承振动信号的故障特征。实验结果表明,LMD能量熵具有较强的信号表征能力,可以有效提取出滚动轴承故障特征。
In order to judge the running status of rolling bearing effectively in the case of small sample,by using the local mean decomposition( LMD),the rolling bearing vibration signal is decomposed. The complex multi-component signal will be decomposed into multiple single component signals. For the characteristic that the distribution of decomposed single component signal is not uniform in the frequency range,by using the LMD energy entropy,the fault feature of rolling bearing vibration signal is extracted. The experimental results show that LMD energy entropy has a strong signal characterization capability,which can effectively extract the rolling bearing fault characteristic.
In order to judge the running status of rolling bearing effectively in the case of small sample,by using the local mean decomposition( LMD),the rolling bearing vibration signal is decomposed. The complex multi-component signal will be decomposed into multiple single component signals. For the characteristic that the distribution of decomposed single component signal is not uniform in the frequency range,by using the LMD energy entropy,the fault feature of rolling bearing vibration signal is extracted. The experimental results show that LMD energy entropy has a strong signal characterization capability,which can effectively extract the rolling bearing fault characteristic.
引文
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[11]孟宗,李姗姗,王亚超.基于LMD和局域时频熵的旋转机械故障诊断方法[J].计量学报,2015,36(1):77-81.
[12]程军圣,罗颂荣,杨斌,等. LMD能量矩和变量预测模型模式识别在轴承故障诊断中的应用[J].振动工程学报,2013,26(5):751-757.
[13]李琳,张永祥,明廷锋.改进的LMD方法及其在滚动轴承故障诊断中的应用研究[J].振动与冲击,2016,35(8):183-186.
[14]姜久亮,刘文艺,侯玉洁,等.基于内积延拓LMD及SVM的轴承故障诊断方法研究[J].振动与冲击,2016,35(6):104-108.
[2] CHENZ,GAO N,SUN W,et al. A signal based triangular structuring element for mathematical morphological analysis and its application in rolling element bearing fault diagnosis[J]. Shock&Vibration,2014(1):1-16.
[3] FENGG,ZHEN D,TIAN X,et al. A novel method to improve the resolution of envelope spectrum for bearing fault diagnosis based on a wireless sensor node[J]. Mechanisms&Machine Science,2014(23):765-775.
[4]姜久亮,刘文艺,韩继光,等.基于内积延拓LMD的机床滚动轴承故障诊断方法研究[J].制造技术与机床,2015(4):67-72.
[5]邓飞跃,唐贵基.基于时间-小波能量谱样本熵的滚动轴承智能诊断方法[J].振动与冲击,2017,36(9):28-34.
[6]赵志宏,杨绍普.一种基于样本熵的轴承故障诊断方法[J].振动与冲击,2012,31(6):136-140.
[7]王宏超,陈进,董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报,2013,49(1):88-94.
[8] SMITHJ S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2005,2(5):443-454.
[9]王小玲,陈进,从飞云.基于时频的频带熵方法在滚动轴承故障识别中的应用[J].振动与冲击,2012,31(18):29-33.
[10]冯辅周,司爱威,饶国强,等.基于小波相关排列熵的轴承早期故障诊断技术[J].机械工程学报,2012,48(13):73-79.
[11]孟宗,李姗姗,王亚超.基于LMD和局域时频熵的旋转机械故障诊断方法[J].计量学报,2015,36(1):77-81.
[12]程军圣,罗颂荣,杨斌,等. LMD能量矩和变量预测模型模式识别在轴承故障诊断中的应用[J].振动工程学报,2013,26(5):751-757.
[13]李琳,张永祥,明廷锋.改进的LMD方法及其在滚动轴承故障诊断中的应用研究[J].振动与冲击,2016,35(8):183-186.
[14]姜久亮,刘文艺,侯玉洁,等.基于内积延拓LMD及SVM的轴承故障诊断方法研究[J].振动与冲击,2016,35(6):104-108.