基于改进CEEMDAN和优化重构的轴承故障特征提取研究
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摘要
滚动轴承作为旋转设备的关键部件,其性能严重影响设备的运行安全。由于设备工况复杂,反映轴承的故障特征的冲击成分往往被噪声信号所淹没,导致无法有效的提取故障特征。为了更准确的获取滚动轴承的故障信息特征,本文提出一种基于改进的自适应噪声完整集合模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和峭度指标的特征提取方法。首先,利用改进的CEEMDAN方法对分解过程中的各段信号添加自适应白噪声,计算唯一的余量来获得各个固有模态函数(intrinsic mode function),与EEMD(ensemble empirical mode decomposition)相比,其分解过程完整。其次,计算各IMF分量的峭度指标,筛选重构IMF分量集,然后利用重构信号峭度最优指标筛选出最合适的重构信号,最后,通过包络解调获得轴承故障特征。结果表明,该方法具有更好的分解效果,自适应性好,可以更好的抑制噪声,提取轴承故障的冲击成分。
Rolling bearing as a key component of rotating equipment, its performance seriously affect the safe operation of the equipment. As the equipment condition is complex, the impact component of the fault feature is often submerged by the noise signal, therefore the fault feature cannot be extracted effectively. A method was proposed based on improved complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and kurtosis index by this paper. Firstly, the improved CEEMDAN method is used to add adaptive white noise to each signal in the decomposition process, a unique residue was computed to obtain each intrinsic model function(IMF), compared with ensemble empirical mode decomposition(EEMD), the decomposition is complete. Secondly, the kurtosis index is calculated of each IMF to select the reconstructed IMF component and the kurtosis index is used to select the most suitable reconstructed signal. Finally, the bearing fault feature is obtained by envelope demodulation. The results confirm that this method has better decomposition effect, better adaptability and highlight the impact of the bearing fault.
Rolling bearing as a key component of rotating equipment, its performance seriously affect the safe operation of the equipment. As the equipment condition is complex, the impact component of the fault feature is often submerged by the noise signal, therefore the fault feature cannot be extracted effectively. A method was proposed based on improved complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and kurtosis index by this paper. Firstly, the improved CEEMDAN method is used to add adaptive white noise to each signal in the decomposition process, a unique residue was computed to obtain each intrinsic model function(IMF), compared with ensemble empirical mode decomposition(EEMD), the decomposition is complete. Secondly, the kurtosis index is calculated of each IMF to select the reconstructed IMF component and the kurtosis index is used to select the most suitable reconstructed signal. Finally, the bearing fault feature is obtained by envelope demodulation. The results confirm that this method has better decomposition effect, better adaptability and highlight the impact of the bearing fault.
引文
[1]钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社,2002:201.ZHONG BingLin,HUANG Ren.Introduction of Mechanical Fault Diagnosis[M].China Machine Press,Beijing,2002:201(In Chinese).
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[5]王文哲,吴华,王经商,等.基于CEEMDAN的雷达辐射源识别脉内细微特征提取方法[J].北京航空航天大学学报,2015,41(1):1-8.WANG WenZhe,WU Hua,WANG JingShang,et al.Subtle Intrapulse Feature Extraction Based on the CEEMDAN for Radar Emitter Identification[J].Journal of Beijing University of Aeronautics,2015,41(1):1-8(In Chinese).
[6]李锋,林阳阳,晁苏全,等.基于CEEMDAN与信息熵的液压泵故障特征提取方法研究[J].机床与液压,2016,44(19):192-195.LI Feng,LIN YangYang,CHAO SuQuan,WANG Hao,et al.Research on Feature Extraction Method of Hydraulic Pump Based on CEEMDAN and Information Entropy[J].Machine Tool and Hydraulics,2016,44(19):192-195(In Chinese).
[7]Golafshan R,Sanliturk K Y.SVD and Hankel matrix based denoising approach for ball bearing fault detection and its assessment using artificial faults[J].Mechanical Systems and Signal Processing,2016,70(71):36-50.
[8]苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.SU WenSheng,WANG FengTao,ZHANG ZhiXin,et al.Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J].Journal of Vibration and Shock,2010,29(3):18-21(In Chinese).
[9]胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32(11):106-111.HU Ai Jun,MA WanLi,TANG GuiJi.Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion[J].Proceedings of the CSEE,2012,32(11):106-111(In Chinese).
[10]张超,陈建军,郭迅.基于EMMD分解的滚动轴承故障诊断[J].机械强度,2012,34(5):650-656.ZHANG Chao,CHEN Jian Jun,GUO Xun.Composite fault diagnosis for rolling bearing of electric machine based on EMMD[J].Journal of Mechanical Strength,2012,34(5):650-656(In Chinese).
[11]Rilling G,Flandrin P,Goncalyes P.On empirical mode decomposition and its algorithms[C]//IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing(NSIPL2003),Grado(1),June,2003:8-11.
[12]Marcelo A C,Gaston S,Maria E T.Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J].Biomedical Signal Processing and Control,2014(14):19-29.
[2]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proceedings of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences,1998(454):903-955.
[3]Wu Z H,Huang N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[4]Torres M E,Colominas M A,Schlotthuer G,et al.A complete ensemble empirical mode decomposition with adaptive noise[J].Brain Research Bulletin,2011,125(3):4144-4147.
[5]王文哲,吴华,王经商,等.基于CEEMDAN的雷达辐射源识别脉内细微特征提取方法[J].北京航空航天大学学报,2015,41(1):1-8.WANG WenZhe,WU Hua,WANG JingShang,et al.Subtle Intrapulse Feature Extraction Based on the CEEMDAN for Radar Emitter Identification[J].Journal of Beijing University of Aeronautics,2015,41(1):1-8(In Chinese).
[6]李锋,林阳阳,晁苏全,等.基于CEEMDAN与信息熵的液压泵故障特征提取方法研究[J].机床与液压,2016,44(19):192-195.LI Feng,LIN YangYang,CHAO SuQuan,WANG Hao,et al.Research on Feature Extraction Method of Hydraulic Pump Based on CEEMDAN and Information Entropy[J].Machine Tool and Hydraulics,2016,44(19):192-195(In Chinese).
[7]Golafshan R,Sanliturk K Y.SVD and Hankel matrix based denoising approach for ball bearing fault detection and its assessment using artificial faults[J].Mechanical Systems and Signal Processing,2016,70(71):36-50.
[8]苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.SU WenSheng,WANG FengTao,ZHANG ZhiXin,et al.Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J].Journal of Vibration and Shock,2010,29(3):18-21(In Chinese).
[9]胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32(11):106-111.HU Ai Jun,MA WanLi,TANG GuiJi.Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion[J].Proceedings of the CSEE,2012,32(11):106-111(In Chinese).
[10]张超,陈建军,郭迅.基于EMMD分解的滚动轴承故障诊断[J].机械强度,2012,34(5):650-656.ZHANG Chao,CHEN Jian Jun,GUO Xun.Composite fault diagnosis for rolling bearing of electric machine based on EMMD[J].Journal of Mechanical Strength,2012,34(5):650-656(In Chinese).
[11]Rilling G,Flandrin P,Goncalyes P.On empirical mode decomposition and its algorithms[C]//IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing(NSIPL2003),Grado(1),June,2003:8-11.
[12]Marcelo A C,Gaston S,Maria E T.Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J].Biomedical Signal Processing and Control,2014(14):19-29.