引用本文: 罗亦泳, 姚宜斌, 黄城, 张静影. 基于改进VMD的变形特征提取与分析[J]. 武汉大学学报 ( 信息科学版), 2020, 45(4): 612-619.
LUO Yiyong, YAO Yibin, HUANG Cheng, ZHANG Jingying. Deformation Feature Extraction and Analysis Based on Improved Variational Mode Decomposition[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 612-619.
 Citation: LUO Yiyong, YAO Yibin, HUANG Cheng, ZHANG Jingying. Deformation Feature Extraction and Analysis Based on Improved Variational Mode Decomposition[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 612-619.

## Deformation Feature Extraction and Analysis Based on Improved Variational Mode Decomposition

• 摘要: 变形数据特征提取与分析是建筑物变形预测、预警及机理解释中的关键问题。基于改进变分模态分解算法（improved variational mode decomposition，IVMD）构建变形特征提取及分析新方法。利用样本熵、中心频率比及相关系数确定变分模态分解的K值建立IVMD，并应用于仿真信号、桥梁变形特征提取及分析。实验结果表明，IVMD能准确地提取仿真信号中包含的分量信号，各项指标均优于经验模态分解（empirical mode decomposition，EMD）和小波变换方法，理论上验证了IVMD算法的可靠性。IVMD能较为准确地提取桥梁固有频率及阻尼特性参数，并且较好地提取到桥梁受温度变化、多路径效应及其他环境影响引起的变形特征信息，证实了IVMD用于变形特征提取与分析的有效性。

Abstract: Deformation data feature extraction and analysis is a key issue to be solved in building deformation prediction, early warning and mechanism interpretation. For this reason, An improved variational mode decomposition algorithm(IVMD)is established to construct a new method of feature extraction and analysis of deformation data. The K value of variational mode decomposition is determined by sample entropy, center frequency ratio and correlation coefficient to establish improved variational mode decomposition, and the new algorithm is tested by simulation signal and bridge deformation monitoring data. The results show that IVMD can accurately extract the component signals contained in the simulation signals, and its correlation coefficient (R) and root mean square error(RMSE) indexes are superior to empirical mode decomposition (EMD) and wavelet transform methods. The reliability of IVMD algorithm is verified theoretically. Based on IVMD, the natural frequency and damping characteristic parameters of the bridge can be extracted accurately, and the deformation characteristic information of the bridge caused by temperature change, multi-path effect and other environmental impacts can be extracted well, which confirms the validity of IVMD in feature extraction and analysis of deformation monitoring data.

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