基于二次CEEMDAN与CCJC的滚动轴承故障冲击特征提取  

Shock Fault Characteristics Extraction of Rolling Bearings Based on Secondary CEEMDAN and CCJC

作　　者：张亢[1] 曹振华 刘鹏飞 陈向民 牛晓瑞 ZHANG Kang;CAO Zhenhua;LIU Pengfei;CHEN Xiangmin;NIU Xiaorui(School of Energy and Power Engineering,Changsha University of Science and Technology,Changsha 410114,China;Big Data Division,Hunan Datang Xianyi Technology Co.,Ltd.,Changsha 410118,China)

机构地区：[1]长沙理工大学能源与动力工程学院,长沙410114 [2]湖南大唐先一科技有限公司大数据事业部,长沙410118

出　　处：《噪声与振动控制》2025年第1期112-118,247,共8页Noise and Vibration Control

基　　金：湖南省自然科学基金资助项目(2018JJ3541);湖南省教育厅科学研究资助项目(21B0347,20B019)。

摘　　要：滚动轴承故障振动信号的成分复杂多样,且受噪声和传递路径的影响,导致从中提取表征故障的周期性冲击成分难度很大。对此,利用自适应噪声完全集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)良好的非平稳非线性数据处理能力,首先将原始轴承振动信号中的各种成分予以分离,在此基础上,提出相关系数跳变准则(Correlation Coefficient Jump Criterion,CCJC)区别以故障周期性冲击成分为主的分量,以及以噪声和转频成分为主的分量,并通过二次分解二次重构的方式,最大限度去除噪声与转频相关成分,最终得到提纯的滚动轴承故障周期性冲击信号。通过对滚动轴承故障仿真信号和基准数据的分析,表明所提方法可以准确高效提取轴承故障周期性冲击成分;对滚动轴承实验振动信号进行分析,并与经典方法对比,验证所提方法的优势及其良好的工程应用前景。The components of rolling bearing fault vibration signal are complex and various,and are affected by noise and transmission path,which leads to the difficulty for extracting the periodic shock components that characterize the fault.In this paper,the excellent non-stationary and nonlinear data processing ability of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)was utilized to overcome this difficulty.Firstly,different kinds of components in original bearing vibration signal were separated.On this basis,the correlation coefficient jump criterion(CCJC)was proposed to distinguish the components dominated by fault periodic shock and those dominated by noise and related rotating frequency.Furthermore,the noise and rotating frequency related components were removed as much as possible by the way of secondary decomposition and secondary reconstruction.Finally the purified rolling bearing fault periodic shock signal was obtained.By analyzing rolling bearing fault analogue signal and basic data,the results show that the proposed method can extract the fault periodic shock components accurately and efficiently.The rolling bearing experimental vibration signals were analyzed,the results were compared with those of classical method.The results show the advantages of the proposed method and its good prospect of engineering application.

关 键 词：故障诊断 滚动轴承 振动信号 周期性冲击特征 自适应噪声完全集合经验模态分解 相关系数跳变准则 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]