基于总体平均经验模态分解与1.5维谱的滚动轴承故障诊断方法
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摘要
针对滚动轴承早期故障冲击信号微弱,强噪声干扰下故障特征难以提取等问题,提出了基于总体平均经验模态分解(EEMD)与1.5维谱的滚动轴承故障诊断方法。由于经验模态分解(EMD)在对信号进行分解时容易产生模态混叠现象,引入总体平均经验模态分解(EEMD)。首先将最小熵解卷积(MED)作为前置滤波器,对原始信号进行降噪处理,再利用1.5维谱对经过EEMD分解得到的较为敏感的本征模态函数进行分析,得到各个分量的1.5维包络谱,最终判断轴承是否存在故障。通过仿真信号及实验信号验证了文中所论方法的可行性和有效性。
For the problems such as weak early shock signal of rolling bearing and difficulty in extracting fault features under strong noise interference,a method of rolling element bearing fault diagnosis based on EEMD and 1.5-dimensional spectrum is proposed,The empirical mode decomposition method is easy to produce false component and the mode mixing phenomenon,and the improved algorithm based on EEMD is used. First,use minimum entropy deconvolution(MED) as a prefilter to denoise the original signal,and then use 1.5-dimensional spectrum pairs to pass EEMD. The more sensitive intrinsic modal functions obtained by the decomposition are analyzed to obtain the 1.5-dimensional envelope spectrum of each component. Finally,it is judged whether the bearing has a fault. The feasibility and effectiveness of the proposed method are verified by simulation signals and experimental signals.
For the problems such as weak early shock signal of rolling bearing and difficulty in extracting fault features under strong noise interference,a method of rolling element bearing fault diagnosis based on EEMD and 1.5-dimensional spectrum is proposed,The empirical mode decomposition method is easy to produce false component and the mode mixing phenomenon,and the improved algorithm based on EEMD is used. First,use minimum entropy deconvolution(MED) as a prefilter to denoise the original signal,and then use 1.5-dimensional spectrum pairs to pass EEMD. The more sensitive intrinsic modal functions obtained by the decomposition are analyzed to obtain the 1.5-dimensional envelope spectrum of each component. Finally,it is judged whether the bearing has a fault. The feasibility and effectiveness of the proposed method are verified by simulation signals and experimental signals.
引文
[1]葛明涛,王霞,刘爱荣.基于1.5维Teager能量谱的滚动轴承故障诊断[J].机械设计与研究,2015,31(5):62-66.
[2]张龙,张磊,熊国良,等.基于多尺度熵和神经网络的滚动轴承故障诊断[J].机械设计与研究,2014,30(5):96-98.
[3]LIU F,RUAN X E.Wavelet-based diffusion approaches for signal de-noising[J].Signal Processing,2007,87:1138-1146.
[4]张建峰,王志华.基于EEMD降噪和HMM的采煤机摇臂滚动轴承故障诊断[J].煤矿机械,2016,37(1):205-207.
[5]蔡艳平,李艾华,石林锁,等.基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J].振动与冲击,2011(2):167-172.
[6]WU Z H N E.Ensemble empirical mode decomposition:A noise assisted data analysis method[J].Adv.Adapt.Data Anal,2009,1(1):1-41.
[7]陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012(15):82-86.
[8]沈长青,谢伟达,朱忠奎,等.基于EEMD和改进的形态滤波方法的轴承故障诊断研究[J].振动与冲击,2013(2):39-43.
[9]唐贵基,王晓龙.基于EEMD降噪和1.5维能量谱的滚动轴承故障诊断研究[J].振动与冲击,2014(1):6-10.
[10]WIGGENS R A.Minimum entropy deconvolution[J].Geophysical Prospecting for Petrole.1980,16(1):21-35.
[11]王宏超.基于稀疏分解及图像稀疏表征的滚动轴承微弱故障诊断[D].上海:上海交通大学,2015.
[12]SAWALHI N R R B.The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J].Mechanical Systems and Signal,2007,21(6):2612-2633.
[13]郑近德,程军圣,杨宇.改进的EEMD算法及其应用研究[J].振动与冲击,2013,32(21):21-26.
[14]刘永强,李翠省,廖英英.基于EEMD和自相关函数峰态系数的轴承故障诊断方法[J].振动与冲击,2017,36(2):111-116.
[15]QIU H,LEE J,LIN J.Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J].Journal of Sound and Vibration,2006,289(1):1066-1090.
[2]张龙,张磊,熊国良,等.基于多尺度熵和神经网络的滚动轴承故障诊断[J].机械设计与研究,2014,30(5):96-98.
[3]LIU F,RUAN X E.Wavelet-based diffusion approaches for signal de-noising[J].Signal Processing,2007,87:1138-1146.
[4]张建峰,王志华.基于EEMD降噪和HMM的采煤机摇臂滚动轴承故障诊断[J].煤矿机械,2016,37(1):205-207.
[5]蔡艳平,李艾华,石林锁,等.基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J].振动与冲击,2011(2):167-172.
[6]WU Z H N E.Ensemble empirical mode decomposition:A noise assisted data analysis method[J].Adv.Adapt.Data Anal,2009,1(1):1-41.
[7]陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012(15):82-86.
[8]沈长青,谢伟达,朱忠奎,等.基于EEMD和改进的形态滤波方法的轴承故障诊断研究[J].振动与冲击,2013(2):39-43.
[9]唐贵基,王晓龙.基于EEMD降噪和1.5维能量谱的滚动轴承故障诊断研究[J].振动与冲击,2014(1):6-10.
[10]WIGGENS R A.Minimum entropy deconvolution[J].Geophysical Prospecting for Petrole.1980,16(1):21-35.
[11]王宏超.基于稀疏分解及图像稀疏表征的滚动轴承微弱故障诊断[D].上海:上海交通大学,2015.
[12]SAWALHI N R R B.The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J].Mechanical Systems and Signal,2007,21(6):2612-2633.
[13]郑近德,程军圣,杨宇.改进的EEMD算法及其应用研究[J].振动与冲击,2013,32(21):21-26.
[14]刘永强,李翠省,廖英英.基于EEMD和自相关函数峰态系数的轴承故障诊断方法[J].振动与冲击,2017,36(2):111-116.
[15]QIU H,LEE J,LIN J.Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J].Journal of Sound and Vibration,2006,289(1):1066-1090.