三点对称差分能量算子与经验小波变换在轴承故障诊断中的应用
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摘要
实际应用中研究机械系统的工作状态时,通常会对其所产的信号进行研究分析,从而得出相关结论。这些由机械系统产生的信号一般含有多种不同波动的混合成分,为了得出可靠的结论,必须从复合信号和背景噪声中分离出有物理意义的成分。因此引入一种新的故障提取方法,首先利用一种较新的模态分解算法——经验小波变换,将一组信号分解成多个具有紧支撑傅里叶频谱的调幅-调频(AM-FM)分量;然后利用K-L散度值挑选出具有物理意义的分量;最后将挑选出的分量通过三点对称差分能量算子运算,得到其能量谱的同时也能得到瞬时频率,从而提取出故障特征。将该方法用于模拟信号和实际轴承故障信号,并且同之前的方法进行对比。结论表明,该方法不仅能很好的提取轴承故障特征,而且证明该方法具有更好的优越性。
The working conditions of mechanic system in real life are generally studied by analysis of signals so that the exact conclusions will be drawn.These signals emanating from mechanic system commonly contain a mixture of different oscillations.For a reliable conclusion,it is necessary to separate a set of physically meaningful modes from the mixture and background noise.Based on that,a new method for bearing fault extraction is proposed in this paper.At first,a novel decomposition algorithm named empirical wavelet transform(EWT) is employed to decompose the fault signal into a set of AM-FM components that have a compact support Fourier spectrum.And then,K-L divergence method is used to select the sensitive component.Finally,the fault characteristic frequency is extracted by a new demodulation method called energy operator of symmetrical differencing( DEO3S) that can restrain the end effect,and the instantaneous frequency is obtained at the same time.The results of the simulation and bearing fault diagnosis experiments indicate that the method can effectively extract fault characteristic frequency,certifying its feasibility and superiority in comparison with the previous methods.
The working conditions of mechanic system in real life are generally studied by analysis of signals so that the exact conclusions will be drawn.These signals emanating from mechanic system commonly contain a mixture of different oscillations.For a reliable conclusion,it is necessary to separate a set of physically meaningful modes from the mixture and background noise.Based on that,a new method for bearing fault extraction is proposed in this paper.At first,a novel decomposition algorithm named empirical wavelet transform(EWT) is employed to decompose the fault signal into a set of AM-FM components that have a compact support Fourier spectrum.And then,K-L divergence method is used to select the sensitive component.Finally,the fault characteristic frequency is extracted by a new demodulation method called energy operator of symmetrical differencing( DEO3S) that can restrain the end effect,and the instantaneous frequency is obtained at the same time.The results of the simulation and bearing fault diagnosis experiments indicate that the method can effectively extract fault characteristic frequency,certifying its feasibility and superiority in comparison with the previous methods.
引文
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[2]郭小青,李东新,田正宏,等.基于噪声信号的振捣棒工作状态判定方法[J].国外电子测量技术,2016,35(8):15-18.GUO X Q,LI D X,TIAN ZH H,et al.Vibrator rod state determining method based on the noise signal[J].Foreign Electronic Measurement Technology,2016,35(8):15-18.
[3]普亚松,郭德伟,张文斌.故障诊断技术在煤矿机械设备中的应用[J].工矿自动化,2015,41(4):36-39.JIN Y S,GUO D W,ZHANG W B.Application offault diagnosis technologies in coal mine machiney[J].Industry and Mine Automation,2015,41(4):36-39.
[4]万晓凤,胡海林,余运俊,等.基于独立量分析的NPC光伏逆变器故障诊断[J].电子测量与仪器学报,2016,30(12):1915-1924WAN X F,HU H L,YU Y J,et al.Fault diagnosis of NPC photovoltaic inverter based on independent component analysis[J].Journal of Electronic Measurement and Instrumentation,2016,30(12):1915-1924.
[5]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J].The Royal Society,1998,454(1971):903-995.
[6]FLANDRIN P,RILLING G,GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2):112-114.
[7]DAVIES M E,JAMES C J.Source separation using single channel ICA[J].Signal Processing,2007,87(8):1819-1832.
[8]WU ZH H,HUANG N E.Ensemble empirical mode decomposition:A noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[9]WANG H,CHEN J,DONG G.Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform[J].Mechanical Systems&Signal Processing,2014,48(1-2):103-119.
[10]CNOSSEN I,FRANZKE C.The role of the Sun in longterm change in the F2 peak ionosphere:New insights from EEMD and numerical modeling[J].Journal of Geophysical Research:Space Physics,2015,119(10):8610-8623.
[11]CHEN X,LIU A,MCKEOWN M J,et al,An EEMDIVA framework for concurrent multi-dimensional EEG and Unidimensional kinematic data analysis[J].IEEE Transactions on Bomedical Engineering,2014,61(7):2187-298.
[12]KRINIDIS S,KRINIDIS M,CHATZIS V.An unsupervised image clustering method based on EEMD image histogram[J].Journal of Information Hiding&Multimedia Signal Processing,2012,3(2):151-163.
[13]TONG W,ZHANG M,YU Q,et al.Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal[J].Journal of Applied Geophysics,2012,83(6):29-34.
[14]YEH J R,SHIEH J S,HUANG N E.Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
[15]GILLES J.Empirical wavelet transform[J].IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
[16]李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J].仪器仪表学报,2014,35(11):2423-2432.LI ZH N,ZHU M,ZHU L L,et al.Mechanical fault diagnosis method based on empirical wavelet transform[J].Chinese Journal of Scientific Instrument,2014,35(11):2423-2432.
[17]孟宗,李姗姗,季艳.基于对称差分能量算子解调的局部均值分解端点效应抑制方法[J].机械工程学报,2014,50(13):80-87.MENG Z,LI SH SH,JI Y.Restraining method for endeffect of local mean decomposition based on energy operator demodulation of symmetrical differencing[J].Journal of Mechanical Engineering,2014,50(13):80-87.
[18]HUANG N E,WU ZH H,LONG S R,et al.On instantaneous frequency[J].Advances in Adaptive Data Analysis,2009,1(2):177-229.
[19]JABLOUN F,CETIN A E.The teager energy based feature parameters for robust speech recognition in car noise[C].IEEE International Conference on Acoustics,Speech&Signal Processing 1999:273-27.
[20]HOU T Y,SHI Z Q.Adaptive data analysis via sparse time-frequency representation[J].Advances in Adaptive Data Analysis,2011,3(1):1-28.
[21]韩中和,李文华.基于K-L散度的EMD虚假分量识别方法[J].中国电机工程学报,2012,32(11):112-117.HAN ZH H,LI W H.A false component identification method of EMD based on Kullback-Leibler divergence[J].Proceeding of the CSEE,2012,32(11):112-117.