(南京工业大学 机械与动力工程学院，南京，210009)
摘 要: 为较好地提取故障信号，提出一种集成经验模式分解(EEMD)和主元分析相结合的降噪方法，给出EEMD自适应分解后本征模函数(IMF)的选择方法，将提取出的IMF分量进行信号重构，从而达到降噪目的。将多尺度主元分析的EEMD降噪、基于峭度准则的EEMD降噪以及基于相关系数准则的EEMD降噪方法分别对仿真信号和回转支承故障信号降噪性能进行对比。研究结果表明:基于多尺度主元分析的EEMD降噪方法具有更高的信噪比(SNR)，提取出更能反映真实故障信息的特征，具有一定的实际工程应用价值。
关键字: 回转支承;主元分析;集成经验模式分解;滤波;振动信号
（College of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 210009, China）
Abstract:In order to extract the fault signal better, a new denoising method based on multi-scale principal component analysis (MSPCA) and the ensemble empirical mode decomposition (EEMD) were proposed. Then a new intrinsic mode functions (IMFs) selection strategy was proposed, which combined the merits of ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA). Finally, vibration signal was reconstructed by the selected IMFs. In order to test the performance of the proposed denoising method, a comparison of the denoising method based on EEMD-kurtosis criterion and EEMD-correlation coefficient criterion was studied. The proposed method based on MSPCA and EEMD was validated by the simulated signals and practical fault signals of slewing bearing. The results show that the method for vibration signal filtering is more effective than other the two denoising methods. It can more effective to improve the signal to noise ratio (SNR) and extract fault characteristic information. Hence, it has powerful value for engineering application.
Key words: slewing bearing; principal component analysis; ensemble empirical mode decomposition; de-noising; vibration signal
