基于CEEMD-WPT的滚动轴承特征提取算法  

Feature Extraction of Rolling Bearing Based on CEEMD-WPT

作　　者：王丽华[1] 陶润喆 张永宏[1] 赵晓平[2] 谢阳阳[1] 

机构地区：[1]南京信息工程大学信息与控制学院,南京210044 [2]南京信息工程大学江苏省网络监控中心,南京210044

出　　处：《振动．测试与诊断》2017年第1期181-188,共8页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51405241;51505234;51575283)

摘　　要：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. © 2017, Editorial Department of JVMD. All right reserved.

关 键 词：滚动轴承 小波包变换 互补总体平均经验模态分解 特征提取 

分 类 号：TN911[电子电信—通信与信息系统] TH165[电子电信—信息与通信工程]