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利用窗口傅里叶分析对噪声信号进行频域分析可提取噪声的部分特征 ,但窗口傅氏变换对不同的频率成分在时域上的取样步长是不变的 ,不能聚焦到对象的任意细节 ,不利于噪声信号特征的提取 .本文利用小波分析 (Wavelet Analysis) ,对高频成分采用逐渐精细的时域或空域取样步长 ;利用 Mallat算法根据半波带滤波器 H、G实现信号的多尺度分解 .本文的目的是提取噪声特征 ,故选取 Daubechies构造的有限脉冲响应滤波器 { hn(k) } 2 n- 1 k=0 ,n=2 ,3,… ,作为低通滤波器 ,它对应的Φ (x)和Ψ (x)是紧支的 .考虑系统工作的实时性 ,取n=2 ,它对应的是四抽头滤波器 ,对风机在断相和正常运行下辐射噪声特征进行提取 .为了进一步分析噪声源辐射噪声信号在各频率段的能量特征 ,用 Bartlet平均周期图法对各级分解中的近似信号和细节信号进行功率谱估计 .
The window Fourier analysis of the noise signal in the frequency domain analysis can extract part of the noise characteristics, but the window Fourier transform for different frequency components in the time domain sampling step is constant, can not focus on any details of the object , Is not conducive to the extraction of the noise signal characteristics.In this paper, using Wavelet Analysis (Wavelet Analysis), for high-frequency components using the finer time or space sampling steps; Mallat algorithm based on half-band filter H, G to achieve more signals Scale decomposition. The purpose of this paper is to extract the noise characteristics. Therefore, the finite impulse response filter {hn (k)} 2 n-1 k = 0, n = 2, The corresponding Φ (x) and Ψ (x) are tight support.Considering the real-time performance of the system, take n = 2, which corresponds to the four-tap filter, the fan in the phase failure and normal operation of the radiation noise characteristics In order to further analyze the energy characteristics of the radiated noise signal of each noise source in each frequency band, the Bartlett average periodogram method is used to estimate the power spectrum of the approximate signal and the detail signal in each level of decomposition.