基于时延自相关预处理的Hilbert-Huang变换解调

针对具有噪声干扰的旋转机械故障振动信号解调问题，提出基于时延自相关运算和经验模态分解（
EMD）方法相结合的新方法.讨论了时延相关算法的降噪原理、离散信号时延相关算法和Hilbert Huang变
换理论.采用矩形窗截断故障振动信号自相关函数的无偏估计, 获取较长时间差的部分，得到时延相关函
数.利用EMD方法对时延相关函数进行自适应滤波, 得到固有模态函数（IMF），对IMF进行Hilbert变换，
求得解调结果.不同噪声强度仿真数据和滚动轴承故障振动信号实验数据分析表明，该方法比直接解调或
仅采用时延相关解调更能有效抑制噪声，凸现信号调制信息.

Hilbert-Huang transform demodulation based on delayed autocorrelation pretreatment

To deal with the demodulation problem of noised rolling machine defect vibration signal,a new method based on time delayed autocorrelation theory and empirical mode decomposition(EMD) method was presented.The delayed autocorrelation denoising principle,delayed autocorrelation algorithm of discrete signal and Hilbert-Huang transform theory were discussed.After the unbiased estimation of the signal's autocorrelation function was intercepted by rectangle window,the function part with long time interval was extracted to yield the time delayed autocorrelation function.The intrinsic mode function(IMF) were extracted by filtering adaptively the wave in the function with EMD method,and Hilbert transform was applied in IMF to calculate the demodulation data.The conclusion was verified by simulation signals with different noise intensity and rolling bearing's defect vibration signal.Analysis shows that the proposed method is more effective than direct modulation or time delayed autocorrelation demodulation in denoising or clarifying modulation information.