一本介绍旋转机械故障诊断的论文

小波阈值法去噪的程序，其中有关于信噪比和均方根误差的公式(wavelet denoising)

这是一本介绍旋转机械故障诊断的论文，采用小波变换方法提取故障特诊，我导师的一个项目就是用的这种特征提取方法，比较成功。(this is a book that introduce the matheds of error dector and we have tested it in our project.)

kalman 卡尔曼滤波C代码 用于滤除高斯白噪声的滤波 测试真是可用。(The kalman Kalman filter C code for filtered white Gaussian noise filtering test is really available.)

利用小波提升算法实现了第二代小波变换,将之应用在信号去噪中,实现了数据的边界对称延拓,去噪效果较好.(Wavelet Lifting algorithm using the second generation wavelet transform, to be used in signal de-noising, the realization of the data symmetric boundary extension, denoising better.)

小波包分解和重构的过程，在齿轮箱中的应用(Wavelet packet decomposition and reconstruction of the signal in the application of matlab, and energy, frequency )

对信号f(x)进行三层小波变换，得到小波系数d1,d2,d3和逼近系数a3，我想求出各个频率段的能量（能量=小波系数的平方和），很不错的应用程序(The signal f (x) for three wavelet transform, wavelet coefficients d1, d2, d3 and approximation coefficients a3, I would like to find the energy of each frequency band (energy = sum of squared wavelet coefficients), very good application)

Wavelet compression algorithm. Start by computing a complete wavelet-transform using the cohen-daubechies-fevereau (2,2) biorthogonal wavelet (which incidentally seems to be the same as used for lossless JPEG2000, but they call it a (5,3)?).

高斯滤波.这段代码主要用于以为信号的高斯滤波。(Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter)

小波去噪,通过对含噪信号进行四层小波分解，此处利用db3小波系列，通过仿真发现可以很好的消除信号中的噪声(Wavelet denoising, the signal through four layers of noisy wavelet decomposition, where the use db3 wavelet series, the simulation found in the well to eliminate the noise signal)

对于非平稳信号，小波多尺度分解师一种有效的信号去噪方法，在小波去噪方法的基础上，提出了基于Lipschitz指数的小波阈值去噪方法(For non-stationary signals, signal de-noising of wavelet multi-scale decomposition ' is an effective method, wavelet denoising method based on proposed wavelet thresholding denoising method based on the Lipschitz exponent)