基于CCWEEMDAN和包络谱熵的轴承故障诊断研究  

Rolling Element Bearing Fault Diagnosis Based on CCWEEMDAN and Envelope Spectrum Entropy

作　　者：林严 林建辉[1] 何刘 熊仕勇 LIN Yan;LIN Jian-hui;HE Liu;XIONG Shi-yong(State Key Laboratory of Traction Power,Southwest Jiaotong University,Sichuan Chengdu 610031,China)

机构地区：[1]西南交通大学牵引动力国家重点实验室

出　　处：《机械设计与制造》2019年第7期127-130,134,共5页Machinery Design & Manufacture

基　　金：国家自然科学基金(61134002)

摘　　要：完全互补小波噪声辅助集总经验模态分解(CCWEEMDAN)是经验模态分解(EMD)的改进算法,是一种噪声辅助的自适应非线性非平稳数据处理方法。噪声辅助能克服EMD方法处理间歇信号出现的“模态混叠”问题。而相比较互补集总经验模态分解(CEEMD),完全互补小波噪声辅助集总经验模态分解能实现更优的性能。在轴承故障诊断的应用中,这里的方法利用小波分解高频段噪声细节成分,添加到原始轴承故障信号中,提取出本征模态信号。利用包络谱熵判断轴承故障导致的冲击响应特征所在本征模态信号,通过对轴承外圈、内圈局部故障状态下的特征提取进行故障诊断,结果表明该方法能有效提取故障冲击响应特征。Complete complementary wavelet ensemble empirical mode decomposition with adaptive noise(CCWEEMDAN) is an improved algorithm of empirical mode decomposition(EMD),it is a kind of noise assisted adaptive nonlinear non-stationary data processing method. Noise assist can overcome the problem of "mode mixing" in the intermittent signal processing by EMD method. Compared with the complementary empirical mode decomposition(CEEMD),the complete complementary wavelet ensemble empirical mode decomposition with adaptive noise can be used to achieve better performance. In the application of bearing fault diagnosis,the wavelet transform is used to decompose the high frequency noise details into the original bearing fault signal to extract the intrinsic mode signal. Using the envelope spectrum entropy response characteristics of intrinsic mode signal caused by the impact of bearing fault judgment,fault diagnosis through the characteristics of the bearing outer ring and the inner ring of local fault condition of extraction,the results show that this method can effectively extract fault impact response characteristics.

关 键 词：完全互补小波噪声辅助集总经验模态分解 模态混叠 包络谱熵 轴承 故障诊断 

分 类 号：TH16[机械工程—机械制造及自动化] TN911.72[电子电信—通信与信息系统]