基于经验模态分解的齿轮箱故障特征提取
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摘要
利用小波去噪阈值法对齿轮箱故障振动信号进行去噪,将经验模态分解(EMD)和快速傅里叶变换(FFT)相结合对齿轮箱故障进行特征提取,此方法适合于对非线性非稳态信号进行自适应的分析.利用小波阈值去噪方法对原始信号进行预处理,将去噪后的信号进行经验模态分解,得到一定数量的本征模态函数(IMF)分量,选取特定的IMF进行FFT,得到相应的功率谱,从而达到提取齿轮箱故障特征频率的目的.对齿轮箱故障信号进行分析,结果表明该方法能够有效地识别出齿轮箱故障特征频率.
Wavelet threshold method was used to de-noise gearbox fault signal,the empirical mode decomposition(EMD)was associated with fast Fourier transform(FFT)to extract gearbox fault feature,and this method was suitable for analyzing non-linear and non-stationary signal adaptively.Firstly,by using wavelet threshold to preprocess the original signal,then the de-noised signal was resolved by empirical mode decomposition to get a certain number of intrinsic mode function(IMF)component and the appropriate power spectrum by selecting a specific IMF transformed by FFT,finally,thereby the aim of extracting gearbox fault characteristic frequency was attained.Through analyzing gearbox fault signal,the results showed that the method could effectively identify the gearbox fault characteristic frequency.
Wavelet threshold method was used to de-noise gearbox fault signal,the empirical mode decomposition(EMD)was associated with fast Fourier transform(FFT)to extract gearbox fault feature,and this method was suitable for analyzing non-linear and non-stationary signal adaptively.Firstly,by using wavelet threshold to preprocess the original signal,then the de-noised signal was resolved by empirical mode decomposition to get a certain number of intrinsic mode function(IMF)component and the appropriate power spectrum by selecting a specific IMF transformed by FFT,finally,thereby the aim of extracting gearbox fault characteristic frequency was attained.Through analyzing gearbox fault signal,the results showed that the method could effectively identify the gearbox fault characteristic frequency.
引文
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[4]张晓楠,曾庆山,万红.基于改进小波去噪和EMD方法的轴承故障诊断[J].测控技术,2014,33(1):23-26.
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[6]贺文杰,Bajolet Julien,Yoann Plassard,等.基于EMD和FFT的齿轮箱故障诊断[J].武汉工程大学学报,2011,33(1):65-70.
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[2]褚福磊,彭志科,冯志鹏,等.机械故障诊断中的现代信号处理方法[M].北京:科学出版社,2009.
[3]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].Proceedings of the Royal Society of London,1998,454(1971):903-955.
[4]张晓楠,曾庆山,万红.基于改进小波去噪和EMD方法的轴承故障诊断[J].测控技术,2014,33(1):23-26.
[5]Donoho D L.Denoising by soft-threshold[J].IEEE Trans on Information Theory,1995,41(3):613-615.
[6]贺文杰,Bajolet Julien,Yoann Plassard,等.基于EMD和FFT的齿轮箱故障诊断[J].武汉工程大学学报,2011,33(1):65-70.
[7]Arsac J.Fourier transforms[M].Englewood:Prentice-Hall,1966.
[8]Blackman R B,Tukey J W.The measurement of power spectra[M].New York:Dover,1958.
[9]Cooley J W,Lewis P A W,Welch P D.Application of the fast Fourier transform to computation of Fourier integrals,Fourier series,and convolution integrals[J].IEEE Transactions on Audio and Electroacoustics,1967,15(2):79-84.