提升EEMD降噪方法及制冷机轴承故障诊断应用研究
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摘要
针对制冷机轴承振动信号被复杂干扰淹没,难以提取有效特征问题,提出一种提升总体经验模态分解(EEMD)的轴承振动信号降噪方法。首先,利用小波包精细分解特性,基于白噪声检验原理提取第一个IMF分量中有用信号;然后,利用噪声和信号主导的本征模态分量(IMFs)与原始信号互相关系数差异巨大的特性,对分解后的IMFs进行区分,分别使用小波包浮动阈值方法和SG滤波算法提取高、低频分量的有用信号,克服了传统EEMD降噪时信号失真、IMFs选择的难题。为了验证方法的有效性,进行了数字仿真与制冷机轴承振动信号应用验证分析,结果表明,所提方法基于一种精细的决策处理方法,可以将淹没在复杂干扰中的有用特征提取出来,为制冷机轴承状态监测提供有效的预处理手段。
The refrigerator bearing vibration signals are submerged by complex interference,and it's difficult to extract effective feature problems. In order to solve this problem,an improved ensemble empirical mode decomposition( EEMD) de-noising method was proposed. Firstly,The useful signals in the first intrinsic mode functions( IMF) component were extracted based on the white noise detection principle. Then,the decomposed intrinsic mode functions were distinguished based on the great difference of the interrelation numbers of noise-original signal and signal-original signal. The useful signals from high and low frequency components were extracted based on wavelet packet floating threshold method and SG filtering algorithm respectively. In order to verify the effectiveness of the proposed method,the simulation and refrigerator bearing vibration signal application verification analysis are carried out. The results show that the proposed method is a fine processing method,which can extract useful features submerged in complex interference and provide effective preprocessing means for the refrigerator bearing state monitoring.
The refrigerator bearing vibration signals are submerged by complex interference,and it's difficult to extract effective feature problems. In order to solve this problem,an improved ensemble empirical mode decomposition( EEMD) de-noising method was proposed. Firstly,The useful signals in the first intrinsic mode functions( IMF) component were extracted based on the white noise detection principle. Then,the decomposed intrinsic mode functions were distinguished based on the great difference of the interrelation numbers of noise-original signal and signal-original signal. The useful signals from high and low frequency components were extracted based on wavelet packet floating threshold method and SG filtering algorithm respectively. In order to verify the effectiveness of the proposed method,the simulation and refrigerator bearing vibration signal application verification analysis are carried out. The results show that the proposed method is a fine processing method,which can extract useful features submerged in complex interference and provide effective preprocessing means for the refrigerator bearing state monitoring.
引文
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[16]聂振国,赵学智.PCA与SVD信号处理效果相似性与机理分析[J].振动与冲击,2016,35(2):12-17.NIE ZH G,ZHAO X ZH.Similarity of signal processing effect between PCA and SVD and its mechanism analysis[J].Journal of Vibration and Shock,2016,35(2):12-17.
[17]GONG Y J,FU Z,XIANG H Y,et al.Denoising vibration signals of food refrigerant air compressor based on wavelet transform[J].Key Engineering Materials,2010(439-440):1037-1041.
[18]陈华舟,潘涛,陈洁梅.多元散射校正与Savitzky-Golay平滑模式的组合优选应用于土壤有机质的近红外光谱分析[J].计算机与应用化学,2011,28(5):518-522.CHEN H ZH,PAN T,CHEN J M.Combination optimization of multiple scatter correction and SavitzkyGolay smoothing modes applied to the near infrared spectroscopy analysis of soil organic matter[J].Computers and Applied Chemistry,2011,28(5):518-522.
[2]BENHMIDENE A,CHAOUACHI B,GABSI S,et al.Modelling of heat flux received by a bubble pump of absorption-diffusion refrigeration cycles[J].Heat&Mass Transfer,2011,47(11):1341-1347.
[3]崔伟成,李伟,孟凡磊,等.奇异值分解降噪结合局部特征尺度分解的轴承故障诊断[J].机械传动,2016,30(5):128-133.CUI W CH,LI W,MENG F L,et al.Bearing fault diagnosis based on singular value decomposition denoising and local characteristic-scale decomposition[J].Journal of Mechanical Transmission,2016,30(5):128-133.
[4]秦波,刘永亮,王建国,等.基于奇异值差分谱降噪与经验模式分解的滚动轴承故障特征提取方法[J].机床与液压,2016,44(11):168-172.QIN B,LIU Y L,WANG J G,et al.Singular values difference spectrum denoising combined with empirical mode decomposition based rolling bearing fault feature extraction method[J].Machine Tool&Hydraulics,2016,44(11):168-172.
[5]张龙,胡俊锋,熊国良.基于加权非局部平均算法的滚动轴承故障诊断[J].振动与冲击,2016,35(19):156-161.ZHANG L,HU J F,XIONG G L.Fault diagnosis of rolling bearing based on weighted nonlocal means algorithm[J].Journal of Vibration and Shock,2016,35(19):156-161.
[6]MAY V,KELLER Y,SHARON N,et al.An algorithm for improving non-local means operators via low-rank approximation[J].IEEE Transactions on Image Processing,2016,25(3):1340-1353.
[7]朱怡,蒋思源.广义S变换时频谱SVD降噪的滚动轴承故障冲击特征提取方法[J].轴承,2016(11):53-57.ZHU Y,JIANG S Y.Rolling bearing default impact feature extracting method based on generalized Stransform time-frequency spectrum denoised by SVD[J].Bearing,2016(11):53-57.
[8]孙红星,张洋.机械轴承故障信号提取仿真研究[J].计算机仿真,2016,33(3):419-423.SUN H X,ZHANG Y.Simulation study on fault signal extraction of mechanical bearing[J].Computer Simulation,2016,33(3):419-423.
[9]王红军,万鹏.基于EEMD和小波包变换的早期故障敏感特征获取[J].北京理工大学学报,2013,33(9):945-950.WANG H J,WAN P.Sensitive features extraction of early fault based on END and WPT[J].Transactions of Beijing Institute of Technology,2013,33(9):945-950.
[10]MANUEL B V,WENG B W,BARNER K E.ECG signal denoising and baseline wander correction based on the empirical mode decomposition[J].Computers in Biology and Medicine,2008,38(1):1-13.
[11]张婷,李双田.改进的EEMD算法在时域航空电磁信号降噪中的研究[J].信号处理,2016,32(7):771-778.ZHANG T,LI SH T.Research of improved ENDalgorithm for time-domain airborne electromagnetic signal de-noising[J].Journal of Signal Processing,2016,32(7):771-778.
[12]邓青林,赵国彦.基于EEMD和小波的爆破振动信号降噪[J].爆破,2015,32(4):33-37.DENG Q L,ZHAO G Y.De-noising of blast vibration signal based on END and wavelet[J].Blasting,2015,32(4):33-37.
[13]王文波,张晓东,汪祥莉.基于主成分分析的经验模态分解消噪方法[J].电子学报,2013,41(7):1425-1430.WANG W B,ZHANG X D,WANG X L.Empirical mode decomposition de-noising method based on principal component analysis[J].Acta Electronica Sinica,2013,41(7):1425-1430.
[14]LI H Y,MARCHANT B P,WEBSTER R.Modelling the electrical conductivity of soil in the Yangtze delta in three dimensions[J].Geoderma,2016(269):119-125.
[15]李琳,张永祥,刘树勇.改进EMD-小波分析的转子振动信号降噪方法[J].噪声与振动控制,2015,35(2):170-174.LI L,ZHANG Y X,LIU SH Y.Denoising of rotor vibration signals based on improved EMD-wavelet analysis[J].Noise and Vibration Control,2015,35(2):170-174.
[16]聂振国,赵学智.PCA与SVD信号处理效果相似性与机理分析[J].振动与冲击,2016,35(2):12-17.NIE ZH G,ZHAO X ZH.Similarity of signal processing effect between PCA and SVD and its mechanism analysis[J].Journal of Vibration and Shock,2016,35(2):12-17.
[17]GONG Y J,FU Z,XIANG H Y,et al.Denoising vibration signals of food refrigerant air compressor based on wavelet transform[J].Key Engineering Materials,2010(439-440):1037-1041.
[18]陈华舟,潘涛,陈洁梅.多元散射校正与Savitzky-Golay平滑模式的组合优选应用于土壤有机质的近红外光谱分析[J].计算机与应用化学,2011,28(5):518-522.CHEN H ZH,PAN T,CHEN J M.Combination optimization of multiple scatter correction and SavitzkyGolay smoothing modes applied to the near infrared spectroscopy analysis of soil organic matter[J].Computers and Applied Chemistry,2011,28(5):518-522.