经验模态分解滤波器组特性及轴承异音识别

Characteristics of EMD Filter Bank and Identification of Abnormal Sound of Ball Bearing

利用经验模态分解滤波器组特性可调整性,结合短时傅里叶变换(STFT)技术识别轴承异音。在研究高斯随机噪声经验模态分解(EMD)的基础上,运用数值方法证实EMD滤波器组特性随判别参数SD改变,指出类似于二进制小波滤波器组特性只是一种特殊条件下的分解现象。根据轴承振动加速度的广谱性质,利用参数SD对EMD滤波器组特性可调性,对滚动轴承振动加速度信号按异音测量要求进行EMD自动频段分解。对前3阶本征模态进行STFT变换,用三维图刻画轴承振动的幅值大小、频率大小、周期和随机分布冲击特性,设定阈值,在时频域上刻画轴承的异音。该方法揭示了轴承异音分布模式,能通过异音识别控制轴承加工质量。

The identification of bearing abnormal sound is proposed by using the filter bank characteristic of empirical mode decomposition(EMD) on a white noise and short time fourier transform(STFT).Based on the EMD numerical experiments on uniformly distributed white noise,it is found that the characteristic of the EMD filter bank will be changed with a stopping criteria SD,and a dyadic filter bank,which resembles wavelet decomposition,is only a particular case of the EMD decomposition.Using the adjustable characteristic of the EMD filter bank,numerical implementations of the EMD adaptively decomposition for ball bearing vibration signal are presented,and the numerical performance of the approach to meet the needs of abnormal sound measurement is realized.Through the STFT on first three orders of intrinsic mode functions,the impact characteristics and instant frequency of bearing vibration are revealed in time domain and frequency domain,and the stochastic and periodic properties of those are intuitively depicted.The abnormal sound level of the ball bearing can be estimated by setting up a threshold value.Besides,the proposed method has advantage of the pattern expression of abnormal sound