APLCD-WPT在滚动轴承特征提取算法中的应用
Feature Extraction of Rolling Bearing Based on APLCD-WPT

针对滚动轴承特征频率提取问题，提出自适应部分集成局部特征尺度分解（adaptive partly-ensemble local charact-eristic-scale decomposition，简称APLCD）与小波包变换（wavelet package transform，简称WPT）结合的APLCD-WPT方法。首先，利用APLCD对滚动轴承振动信号进行处理，通过添加幅值随频率变化的噪声改善信号极值点分布，再提取内禀尺度分量（intrinsic mode component，简称ISC）；其次，对ISC分量中模态混淆部分使用WPT进行修正，提取滚动轴承特征频率信号。应用提出方法对实测的卧式螺旋离心机振动信号进行研究，结果表明，基于APLCD-WPT的算法能够有效地解决模态混淆问题，实现特征频率信号的精确提取。
In order to extracting the characteristic frequency of rolling bearing signal, a feature extraction method based on adaptive partly-ensemble local characteristic-scale decomposition (APLCD) and wavelet package transform (WPT) is proposed, or APLCD-WPT for short. First, APLCD is employed to process vibration signals of rolling bearings, and it can add noise to improve the signal extreme-point distribution in extracting intrinsic mode component by changing the frequency variation. Then, WPT is used to trim less modal mixing problem, which can extract characteristic frequency signal of rolling bearing. Finally, the vibration signal of horizontal spiral centrifuge is analysed based on this method. The results show that APLCD-WPT can effectively suppress the mode mixing to accurately extract the characteristic frequency signal.