基于CEEMD-WPT的滚动轴承特征提取算法
Feature Extraction of Rolling Bearing Based on CEEMD-WPT

为实现对滚动轴承振动信号中特征频率成分的精确提取，提出了将互补总体平均经验模态分解(complementary ensemble empirical mode decomposition,简称CEEMD)与小波包变换(wavelet package transform,简称WPT）相结合即CEMMD-WPT特征信号提取算法。两种方法的结合既有效解决了CEEMD分解后依然存在的模态混叠问题，又消除了进行WPT处理后产生虚假频率分量、频率混淆现象的影响。通过仿真试验验证了该方法的有效性，并应用于实际，取得很好的结果。
Rolling bearings are one of the most widely used and most easily damaged components in mechanical equipment. Extracting the vibration signal of the rolling bearing can give us a better grasp of the equipment’s operational state. In practical applications, traditional wavelet package transform (WPT) due to a defect itself MALLAT algorithm cannot accurately extract the characteristic frequency of the signal. Complementary ensemble empirical mode decomposition (CEEMD) can effectively restrain the mode mixing problem, but cannot completely avoid it. In order to accurately diagnose rolling bearing defects, we propose the WPT-CEMMD feature extraction method, based on CEEMD and WPT. Combining the the two methods could not only effectively solve the problem of mode mixing after CEEMD decomposition, but also eliminate the influence of the spurious frequency component and frequency aliasing after WPT treatment. Both simulations and a case of the working frequency of extraction demonstrated the efficacy of the proposed method.