基于Hermitian小波的信号奇异性识别

机械设备由于故障而诱发的信号往往具有奇异性(Singularity),正确识别奇异性对设备监测诊断十分重要.采用高斯函数的一阶、二阶导数构成复数Hermitian小波进行奇异性识别,具有两个优点:其一是由于Hermitian小波的Fourier变换是实数,对信号进行变换时不会有相位的改变;其二是与Morlet小波相比较,Hermitian小波的实部和虚部振荡次数少,可用较少的数据点对信号进行卷积,从而不会损坏信号的奇异性.本文提出了基于Hermitian小波变换的时间-尺度幅图和相图的信号奇异性识别方法,在大型空气压缩机的齿轮箱撞击摩擦故障诊断中取得了成功的应用.

Recognization of Signal Singularity Based on Hermitian Wavelet Transform

The signal in the nature of singularity is always caused by mechanical fault of equipment. It is important to recognize the singularity correctly for mechanical fault diagnosis. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. There are two advantages of using Hermitian wavelet: First, the complex Hermitian wavelet has a real Fourier transform that will not mix the signal phase with its filter phase; Second, the real part and the imagine part of Hermitian wavelet have less oscillation than Morlet wavelet, so that the convolution operation can be process with fewer number of data points and the signal singularity will not be smeared. The time scale amplitude plot and phase plot based on Hermitian wavelet are presented to detect signal singularities. A successful application has been obtained in impact rub fault diagnosis of large air compressor gearbox.