基于VMD的滚动轴承早期故障诊断方法
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摘要
滚动轴承是旋转机械的重要零部件,当发生早期故障时,难以有效地提取其微弱的故障特征.针对这一问题,提出了优化参数K取值的变分模态分解(variational mode decomposition,VMD)早期故障诊断方法.首先,通过瞬时频率均值判断法确定模态数K的取值,然后用VMD方法对采集的轴承故障信号进行处理.通过筛选轴承故障信号分解得到本征模态函数分量,对其中的敏感分量进行包络谱分析,从而判断轴承的故障类型与严重程度.最后,分别比较EMD和原VMD算法得到的结果.结果表明:优化后的VMD算法能成功地提取滚动轴承早期故障特征,实现轴承早期故障诊断.
Rolling bearings are important parts of rotating machinery. When the early failure occurs,it is difficult to effectively extract the weak fault features. Aiming at this problem,an early fault diagnosis method of variational mode decomposition( VMD) of optimizing the parameter K value was proposed.First,the instantaneous frequency mean judgment method was used to determine the value of modal number K,and then the fault diagnosis signal was processed by VMD method. By analyzing the intrinsic modal function components obtained by decomposing the fault signal of the bearing,the sensitive components were obtained for the envelope demodulation analysis to judge the fault type and severity of the bearing. Finally,the results obtained by the EMD and VMD algorithm were compared. Results show that the optimized VMD algorithm can successfully extract the early fault features of the bearing and achieve the diagnosis of early bearing failure.
Rolling bearings are important parts of rotating machinery. When the early failure occurs,it is difficult to effectively extract the weak fault features. Aiming at this problem,an early fault diagnosis method of variational mode decomposition( VMD) of optimizing the parameter K value was proposed.First,the instantaneous frequency mean judgment method was used to determine the value of modal number K,and then the fault diagnosis signal was processed by VMD method. By analyzing the intrinsic modal function components obtained by decomposing the fault signal of the bearing,the sensitive components were obtained for the envelope demodulation analysis to judge the fault type and severity of the bearing. Finally,the results obtained by the EMD and VMD algorithm were compared. Results show that the optimized VMD algorithm can successfully extract the early fault features of the bearing and achieve the diagnosis of early bearing failure.
引文
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[2] DU Q,YANG S. Application of the EMD method in the vibration analysis of ball bearings[J]. Mechanical Systems&Signal Processing,2007,21(6):2634-2644.
[3]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 A J,MA W L,TANG G J. Fault feature extraction of rolling bearing based on integrated empirical mode decomposition and kurtosis criterion[J]. Proceedings of The Chinese Society for Electrical Engineering,2012,32(11):106-111.(in Chinese)
[4] YEH J R,SHIEH J S,HUANG N E. Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J]. Advanced in Adaptive Data Analysis,2010,2(2):135-156.
[5] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531-544.
[6]杨洪柏,蒋超,石坤举,等.基于变分模态分解参数估计的滚动轴承故障信息提取方法[J].轴承,2016,10:49-52,55.YANG H B,JIANG C,SHI K,et al. Rolling bearing fault information extraction method based on variational mode decomposition parameter estimation[J]. Bearing,2016,10:49-52,55.(in Chinese)
[7]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.LIU X L,WU Y J,ZHEN C G. Rolling bearing fault diagnosis based on variational modal decomposition and fuzzy C-means clustering[J]. Proceedings of The Chinese Society for Electrical Engineering,2015,35(13):3358-3365.(in Chinese)
[8] WANG D,LUO H,GRUNDER O,et al. Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction[J]. Renewable Energy,2017,113:1345-1358.
[9] ZHANG M,JIANG Z,FENG K. Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump[J]. Mechanical Systems&Signal Processing,2017,93(460):460-493.
[10] LEE J,QIU H,YU G,et al. Rexnord Technical Services(2007).‘Bearing Data Set’, IMS, University of Clicinnati. NASA Ames Prognostics Data Repository,NASA Ames,Moffett Field,CA[EB/OL].[2017-08-30]. http:∥ti. arc. nasa. gov/Project/prognostic-datarepository.