孙苗,屈玲,袁立平,吴静,沈玉光

• 1:湖北国土资源职业学院环境与工程学院
• 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
• 3:湖北小城镇发展研究中心
• 4:湖北省地质局地球物理勘探大队

摘要(Abstract)：

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。

关键词(KeyWords)： 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

Abstract:

Keywords:

基金项目(Foundation): 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)~~

作者(Author): 孙苗,屈玲,袁立平,吴静,沈玉光

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