The extraction of weak multi-frequency signal in the background of heavy colored noise is studied. For the multi-frequency signal which cannot be directly decomposed by ensemble empirical mode decomposition (EEMD), further processing is made in the paper. Based on the normalized autocorrelation analysis and EEMD, a new method of weak multi-frequency signal detection is proposed in the article. Firstly, the adaptive noise reduction of multi-frequency signal with strong colored noise is realized by the normalized autocorrelation analysis. Then, the denoised multi-frequency signal is decomposed by the EEMD method. A series of monochromatic intrinsic mode functions are obtained to representing the characteristics of multi-frequency signal. Finally, two mechanical fault experiments are designed to verify the applicability of the proposed method in the fault diagnosis. The vibration signals with bearing pedestal bolt looseness fault, bearing outer raceway and rolling element compound fault were collected, respectively. The experimental results show that the weak multi-frequency fault features in the vibration signals with heavy colored noise are effectively extracted by the new method. The proposed method has a good application prospect in the field of signal processing.

研究了重色噪声背景下微弱多频信号的提取。对于不能通过集成经验模态分解（EEMD）直接分解的多频信号，本文进行了进一步的处理。在归一化自相关分析和EEMD的基础上，提出了一种新的弱多频信号检测方法。首先，通过归一化自相关分析，实现了具有较强色噪声的多频信号的自适应降噪。然后，通过EEMD方法分解经去噪的多频信号。获得了一系列单色本征函数，以表示多频信号的特性。最后，设计了两个机械故障实验，以验证该方法在故障诊断中的适用性。分别采集轴承座螺栓松动故障，轴承外圈滚道和滚动体复合故障的振动信号。实验结果表明，该方法能有效地提取出重色噪声振动信号中的弱多频故障特征。该方法在信号处理领域具有良好的应用前景。实验结果表明，该方法能有效地提取出重色噪声振动信号中的弱多频故障特征。该方法在信号处理领域具有良好的应用前景。实验结果表明，该方法能有效地提取出重色噪声振动信号中的弱多频故障特征。该方法在信号处理领域具有良好的应用前景。