基于EP-CEEMDAN算法的非平稳振动信号时频分析  

Time-frequency Analysis of Non-Stationary Vibration Signals based on EP-CEEMDAN Algorithm

作　　者：孙苗 屈玲 袁立平[1] 吴静 沈玉光 SUN Miao;QU Ling;YUAN Li-ping;WU Jing;SHEN Yu-guang(College of Environment and Engineering,Hubei Land Resources Vocational College,Wuhan 430090,China;Engineering Research Center of Rock-soil Drilling&Excavation and Protection,Ministry of Education of China University of Geosciences,Wuhan 430074,China;Hubei Small Town Development Research Center,Xiaogan 432000,China;Geophysical Exploration Brigade of Hubei Geological Bureau,Wuhan 430056,China;Faculty of Civil Engineering,Hubei Engineering University,Xiaogan 432000,China)

机构地区：[1]湖北国土资源职业学院环境与工程学院,武汉430090 [2]中国地质大学(武汉)岩土钻掘与防护教育部工程研究中心,武汉430074 [3]湖北小城镇发展研究中心,孝感432000 [4]湖北省地质局地球物理勘探大队,武汉430056 [5]湖北工程学院土木工程学院,孝感432000

出　　处：《爆破》2024年第4期150-155,166,共7页Blasting

基　　金：湖北省自然科学基金计划项目(2022CFB334、2022CFB948);岩土钻掘与教育部工程研究中心(202404、202409);湖北省教育厅科学研究计划指导性项目(B2022602);湖北小城镇发展研究中心基金(2024A004)。

摘　　要：针对经验模态分解(Empirical Mode Decomposition,EMD)固有的模态混淆及集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)能在一定程度上抑制模态混淆但由于添加的白噪声无法完全中和,原始信号的完备性无法保证。同时二者均无法免除端点效应的干扰,模态混淆和端点效应导致EMD和EEMD希尔伯特变换得到的时频分析结果失真。提出添加端点处理程序的自适应补充集合经验模态分解算法(Endpoint Processing-Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,EP-CEEMDAN),实施仿真实验对比EMD、EEMD、EP-CEEMDAN对仿真含噪非平稳振动信号的分解结果,并通过多尺度排列熵检测和边际谱分析验证EP-CEEMDAN对端点效应和模态混淆均具有良好的控制效果,从而证明相比EMD和EEMD,EP-CEEMDAN是一种更优良的自适应算法。最后将EP-CEEMDAN应用于实测非平稳振动信号处理中,发现其通过对端点处理后的振动信号在分解的每一阶段添加自适应白噪声,再通过计算唯一的余项信号获得各个固有模态函数(Intrinsic Mode Function,IMF)。EP-CEEMDAN算法得到的IMF端点发散和模态混淆都得到了有效抑制,经希尔伯特变换得到的时频谱在时域和频域均具有较高的分辨率。该结果可用于非平稳振动信号振动特征识别,对进一步分析工程振动危害提供依据。The intrinsic mode confusion of empirical mode decomposition(EMD)and the ensemble empirical mode decomposition(EEMD)can only suppress mode confusion to a limited extent,as the white noise added by EEMD cannot be fully neutralized,which compromises the completeness of the original signal.Additionally,both methods fail to avoid interference from endpoint effects.Modal confusion and endpoint effects lead to distortions in the time-frequency analysis results obtained from the Hilbert transforms of EMD and EEMD.A complete ensemble empirical mode decomposition with adaptive noise and endpoint processing(EP-CEEMDAN)is proposed to address these issues.Simulation experiments were conducted to compare EMD,EEMD,and EP-CEEMDAN decomposition results on simulated vibration signals.Through multiscale permutation entropy detection and marginal spectral analysis,it was verified that EP-CEEMDAN has better control over endpoint effects and mode confusion,proving that EP-CEEMDAN is a more effective adaptive algorithm than EMD and EEMD.Finally,EP-CEEMDAN was applied to the processing of measured non-stationary vibration signals,where adaptive white noise was added at the endpoints of the vibration signals during each stage of decomposition.The method successfully generated various intrinsic mode functions(IMF)by calculating a unique residual signal.The EP-CEEMDAN algorithm effectively suppresses IMF endpoint divergence and modal confusion,while the time-frequency spectrum obtained through the Hilbert transform offers high resolution in both time and frequency domains.This result can be used for vibration feature recognition in non-stationary vibration signals.

关 键 词：经验模态分解 自适应补充集合经验模态分解 模态混淆 端点效应 希尔伯特变换 

分 类 号：O382[理学—流体力学]