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说明：  提出了一种基于复解析小波变换的相位诊断齿轮故障的方法。复解析小波变换将Hilbert变换与小波分析紧密结合在一起,具有自适应分析能力。利用复解析小波变换得到齿轮振动信号的相位,其功率谱图上可提取调制边频带结构来识别故障模式。试验结果表明这种方法可有效应用于齿轮局部故障诊断中。(A new method using the phase based on complex analytical wavelet transform for fault diagnosis of gears was proposed. The phase of vibration signals of gears is got by complex analytical wavelet transform, which combines Hilbert transform with wavelet transform tightly and possesses adaptive analysis ability. By making spectrum analysis of the phase, the sideband structure in power spectrum can be protruded, and the fault pattern can be identified. Experimental results show that the proposed method is effective in local defect detection of gears.)

小波分解与重构，该程序可以实现小波变换的分解与重构。(Wavelet decomposition and reconstruction, the program can achieve wavelet transform decomposition and reconstruction.)

提升小波包学习资料，对了解学习提升小波包，研究算法的改进很有帮助(Lifting wavelet package learning materials, learning to enhance the understanding of wavelet packet of helpful Algorithm)

小波阈值去噪的经典程序，基于小波变换阈值去噪的MATLAB源代码，效果很好。 (Classic wavelet threshold denoising procedure threshold denoising based on wavelet transform of MATLAB source code, works well.)

此示意程序用DWT实现二维小波变换 1.调原始图像矩阵 2.进行二维小波分解 3.分解结果显示 重构源图像及结果显示 (This indicated the procedure using two-dimensional wavelet transform DWT realize one. Transferred the original image matrix 2. To carry out two-dimensional wavelet decomposition 3. Decomposition results show that reconstruction of the source images and results show)

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)

基于墨西哥草帽小波函数进行连续小波变换的matlab变换程序，望对大家有所帮助。(Mexican hat wavelet function based on continuous wavelet transform matlab transformation process, hope to be helpful.)

这是采用小波变换以及压缩感知的理论编写的图像融合程序(This is the wavelet transform and the theory of compressed sensing image fusion program written in)

利用小波包分析信号的matlab程序，大家可以参考(Matlab program using wavelet packet analysis of the signal, we can refer to)

Resumé de l’article: "Early Detection of Fatigue Damage on Rolling Element Bearings Using Adapted Wavelet" par Xavier Chiementin, Fabrice Bolaers, Jean-Paul Dron en franç ais