基于ENEMD与Teager能量算子的轴承早期微弱故障特征提取研究
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摘要
针对经验模态分解(EMD)和集合经验模态分解(EEMD)方法存在模态混叠、噪声残留以及对滚动轴承早期微弱故障特征提取效果不理想的问题,提出一种基于集成噪声重构经验模态分解(ensemble noise-reconstructed empirical mode decomposition, ENEMD)与Teager能量算子的轴承早期微弱故障特征提取方法。首先,使用ENEMD对采集信号进行分解,基于固有噪声分量处理方法实现信号降噪;其次,基于峭度与相关系数的联合准则,提取ENEMD分解的峭度值及相关系数较大的imf分量进行重构;再次,利用Teager能量算子对重构信号进行分析,从而提取到滚动轴承的早期微弱故障特征;最后,基于滚动轴承内、外圈的故障振动信号,开展所提方法与基于EMD和Teager能量算子(EMD-Teager)及基于EEMD和Teager能量算子(EEMD-Teager)方法对比实验。实验结果表明,本方法能有效提取滚动轴承早期微弱故障特征,并取得略优于EMD-Teager和EEMD-Teager能量算子的效果。
Aiming at the problems of mode aliasing, residual noise and unsatisfactory effect for extracting early weak fault features of rolling bearings with empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) methods, a feature extraction method for early weak faults in rolling bearing is proposed, combined ensemble noise-reconstructed empirical mode decomposition(ENEMD) with Teager energy operator in this paper. First of all, the collection signal is decomposed with ENEMD to realize signal denoising based on the intrinsic noise component processing method. Then, the value of ENEMD decomposition and the IMF component with large correlation coefficient are extracted for reconstruction based on the joint criterion of kurtosis and correlation coefficient. After that, Teager energy operator is used to analyze the reconstructed signal and the early weak fault characteristics of rolling bearing can be extracted. Finally, the proposed method is compared with the EMD-Teager method and EEMD-Teager method based on the fault vibration signals of the inner and outer rings in rolling bearings. The experimental results show that the proposed approach can extract the early weak fault characteristics of rolling bearings effectively, and achieves slightly better results than EMD-Teager and EEMD-Teager energy operators.
Aiming at the problems of mode aliasing, residual noise and unsatisfactory effect for extracting early weak fault features of rolling bearings with empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) methods, a feature extraction method for early weak faults in rolling bearing is proposed, combined ensemble noise-reconstructed empirical mode decomposition(ENEMD) with Teager energy operator in this paper. First of all, the collection signal is decomposed with ENEMD to realize signal denoising based on the intrinsic noise component processing method. Then, the value of ENEMD decomposition and the IMF component with large correlation coefficient are extracted for reconstruction based on the joint criterion of kurtosis and correlation coefficient. After that, Teager energy operator is used to analyze the reconstructed signal and the early weak fault characteristics of rolling bearing can be extracted. Finally, the proposed method is compared with the EMD-Teager method and EEMD-Teager method based on the fault vibration signals of the inner and outer rings in rolling bearings. The experimental results show that the proposed approach can extract the early weak fault characteristics of rolling bearings effectively, and achieves slightly better results than EMD-Teager and EEMD-Teager energy operators.
引文
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[12] 曹冲锋,杨世锡,杨将新.大型旋转机械非平稳振动信号的EEMD降噪方法[J].振动与冲击,2009,28(9):33-38.CAO CH F,YANG SH X,YANG J X.De-noising method for non-stationary vibration signals of large rotating machineries based on ensemble empirical mode decomposition[J].Journal of Vibration and Shock,2009,28(9):33-38.
[13] 袁静,訾艳阳,倪修华,等.改进集成噪声重构经验模式分解的微弱时频特征增强方法及应用[J].机械工程学报,2016,52(19):88-94.YUAN J,ZI Y Y,NI X H,et al.Weak time-frequent feature enhancement method using improved ensemble noise-reconstructed empirical mode decomposition and its application[J].Journal of Mechanical Engineering,2016,52(19):88-94.
[14] YUAN J,HE Z,NI J,et al.Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection[J].Journal of Vibration and Acoustics,2013,135(2):021011.
[15] 刘尚坤,唐贵基,何玉灵.Teager能量算子结合MCKD的滚动轴承早期故障识别[J].振动与冲击,2016,35(15):98-102.LIU SH K,TANG G J,HE Y L.Incipient fault diagnosis of rolling bearings based on Teager energy operator and MCKD[J].Journal of Vibration and Acoustics,2016,35(15):98-102.
[16] 向玲,鄢小安.基于改进ITD和峭度准则的滚动轴承故障诊断方法[J].机床与液压,2015,43(11):178-181.XIANG L,YAN X AN.The method of rolling bearing fault diagnosis based on improved intrinsic time-scale decomposition and kurtosis criterion[J].Machine Tool & Hydraulics,2015,43(11):178-181.
[17] 陈仁祥,汤宝平,吕中亮.基于相关系数的 EEMD 转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-547.CHEN R X,TANG B P,LV ZH L.Ensemble empirical mode decomposition de-noising method based on correlation coefficients for vibration signal of rotor system[J].Vibration Test and Diagnosis,2012,32(4):542-547.
[18] 任达千,杨世锡,吴昭同,等.信号瞬时频率直接计算法与Hilbert变换及Teager能量法比较[J].机械工程学报,2013,49(9):42-48.REN D Q,YANG SH X,WU ZH T,et al.Comparison of instantaneous frequency directed computing method and Hilbert Transform and Teager energy method[J].Journal of Mechanical Engineering,2013,49(9):42-48.
[19] 张小龙,张氢,秦仙蓉,等.基于ITD-形态滤波和Teager能量谱的轴承故障诊断[J].仪器仪表学报,2016,37(4):788-795.ZHANG X L,ZHANG Q,QIN X R,et al.Fault diagnosis method for rolling bearing based on ITD-morphological filter and Teager energy spectrum[J].Chinese Journal of Scientific Instrument,2016,37(4):788-795.
[20] 王天金,冯志鹏,郝如江,等.基于Teager能量算子的滚动轴承故障诊断研究[J].振动与冲击,2012,31(2):1-5,85.WANG T J,FENG ZH P,HAO R J,et al.Fault diagnosis of rolling element bearings based on Teager energy operator[J].Journal of Vibration and Acoustics,2012,31(2):1-5,85.
[21] LIU Z W,HE Z J,GUO W,et al.A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery[J].ISA Transactions,2016(61):211-220.
[2] JIANG H K,LI C L,LI H X.An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis[J].Mechanical Systems and Signal Processing,2013,36(2):225-239.
[3] 雷亚国,孔德同,李乃鹏,等.自适应总体平均经验模式分解及其在行星齿轮箱故障检测中的应用[J].机械工程学报,2014,50(3):64-70.LEI Y G,KONG D T,LI N P,et al.Adaptive ensemble empirical mode decomposition and its application to fault detection of planetary gearboxes[J].Journal of Mechanical Engineering,2014,50(3):64-70.
[4] WANG L,LIU Z,MIAO Q,et al.Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis[J].Mechanical Systems & Signal Processing,2018(103):60-75.
[5] 王宏超,陈进,霍柏琦,等.强抗噪时频分析方法及其在滚动轴承故障诊断中的应用[J].机械工程学报,2015,51(1):90-96.WANG H CH,CHEN J,HUO B Q,et al.Noise-resistant time-frequency analysis method and its application in fault diagnosis of rolling bearing[J].Journal of Mechanical Engineering,2015,51(1):90-96.
[6] 张焱,汤宝平,邓蕾,等.基于局域均值分解的自适应滤波滚动轴承故障特征提取[J].振动与冲击,2015,34 (23):25-30.ZHANG Y,TANG B P,DENG L,et al.Fault feature extraction for rolling bearings based on adaptive wavelet filtering and LMD[J].Vibration and Shock,2015,34(23):25-30.
[7] 张刚,李红威.小波-EMD和随机共振级联微弱信号检测[J].电子测量与仪器学报,2018,32(1):57-65.ZHANG G.LI H W.Wavelet-EMD and stochastic resonance cascade weak signal detection[J].Journal of Electronic Measurement and Instrumentation,2018,32(1):57-65.
[8] 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.
[9] 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 W SH,WANG F T,ZHANG ZH X,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.
[10] 张志刚,石晓辉,陈哲明,等.基于改进EMD和滑动峰态算法的滚动轴承故障特征提取[J].振动与冲击,2012,31(22):80-83.ZHANG ZH G,SHI X H,CHEN ZH M,et al.Fault feature extraction of rolling element bearing based on improved EMD and sliding kurtosis algorithm[J].Journal of Vibration and Shock,2012,31(22):80-83
[11] 彭畅,柏林,谢小亮.基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J].振动与冲击,2012,31(20):143-146.PENG CH,BO L,XIE X L.Fault diagnosis method of rolling element bearings based on EEMD,measure-factor and fast kurtogram[J].Journal of Vibration and Shock,2012,31(20):143-146.
[12] 曹冲锋,杨世锡,杨将新.大型旋转机械非平稳振动信号的EEMD降噪方法[J].振动与冲击,2009,28(9):33-38.CAO CH F,YANG SH X,YANG J X.De-noising method for non-stationary vibration signals of large rotating machineries based on ensemble empirical mode decomposition[J].Journal of Vibration and Shock,2009,28(9):33-38.
[13] 袁静,訾艳阳,倪修华,等.改进集成噪声重构经验模式分解的微弱时频特征增强方法及应用[J].机械工程学报,2016,52(19):88-94.YUAN J,ZI Y Y,NI X H,et al.Weak time-frequent feature enhancement method using improved ensemble noise-reconstructed empirical mode decomposition and its application[J].Journal of Mechanical Engineering,2016,52(19):88-94.
[14] YUAN J,HE Z,NI J,et al.Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection[J].Journal of Vibration and Acoustics,2013,135(2):021011.
[15] 刘尚坤,唐贵基,何玉灵.Teager能量算子结合MCKD的滚动轴承早期故障识别[J].振动与冲击,2016,35(15):98-102.LIU SH K,TANG G J,HE Y L.Incipient fault diagnosis of rolling bearings based on Teager energy operator and MCKD[J].Journal of Vibration and Acoustics,2016,35(15):98-102.
[16] 向玲,鄢小安.基于改进ITD和峭度准则的滚动轴承故障诊断方法[J].机床与液压,2015,43(11):178-181.XIANG L,YAN X AN.The method of rolling bearing fault diagnosis based on improved intrinsic time-scale decomposition and kurtosis criterion[J].Machine Tool & Hydraulics,2015,43(11):178-181.
[17] 陈仁祥,汤宝平,吕中亮.基于相关系数的 EEMD 转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-547.CHEN R X,TANG B P,LV ZH L.Ensemble empirical mode decomposition de-noising method based on correlation coefficients for vibration signal of rotor system[J].Vibration Test and Diagnosis,2012,32(4):542-547.
[18] 任达千,杨世锡,吴昭同,等.信号瞬时频率直接计算法与Hilbert变换及Teager能量法比较[J].机械工程学报,2013,49(9):42-48.REN D Q,YANG SH X,WU ZH T,et al.Comparison of instantaneous frequency directed computing method and Hilbert Transform and Teager energy method[J].Journal of Mechanical Engineering,2013,49(9):42-48.
[19] 张小龙,张氢,秦仙蓉,等.基于ITD-形态滤波和Teager能量谱的轴承故障诊断[J].仪器仪表学报,2016,37(4):788-795.ZHANG X L,ZHANG Q,QIN X R,et al.Fault diagnosis method for rolling bearing based on ITD-morphological filter and Teager energy spectrum[J].Chinese Journal of Scientific Instrument,2016,37(4):788-795.
[20] 王天金,冯志鹏,郝如江,等.基于Teager能量算子的滚动轴承故障诊断研究[J].振动与冲击,2012,31(2):1-5,85.WANG T J,FENG ZH P,HAO R J,et al.Fault diagnosis of rolling element bearings based on Teager energy operator[J].Journal of Vibration and Acoustics,2012,31(2):1-5,85.
[21] LIU Z W,HE Z J,GUO W,et al.A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery[J].ISA Transactions,2016(61):211-220.