基于VMD-HHT边际谱的水工结构损伤诊断
Damage Diagnosis of Hydraulic Structure Based on VMD and HHT Marginal Spectrum

为了准确识别水工结构的损伤，提出一种变分模态分解（variational mode decomposition, 简称VMD）和Hilbert-Huang变换（Hilbert-Huang transform, 简称HHT）边际谱相结合的水工结构损伤诊断方法。首先，采用联合的小波阈值和经验模态分解（empirical mode decomposition, 简称EMD）降噪方法对原始信号进行降噪，减小环境噪声对结构损伤特征信息的干扰；其次，运用方差贡献率数据融合算法对降噪后各测点信号进行动态融合，提取结构完整的振动特性信息；然后，采用VMD方法将动态融合信号分解为一系列固态模量（intrinsic mode function, 简称IMF），对各IMF分量进行Hilbert变换，求出融合信号的边际谱；最后，在VMD边际谱的基础上提取一种新的损伤特征向量 损伤灵敏指数，将其与马氏距离相结合对水工结构的损伤类型进行分类，并将该方法应用于悬臂梁模型试验。结果表明：该方法能够有效提取水工结构的损伤特性，准确识别水工结构的损伤和运行状态，为水工结构的安全运行提供了基础。
In order to accurately identify the damage of hydraulic structure, a valid damage diagnosis method combining variational mode decomposition(VMD) and Hilbert-Huang transform (HHT) marginal spectrum is proposed. Firstly,a kind of combined wavelet threshold and empirical mode decomposition (EMD) method is used to filter out the noises, which can reduce the disturbance of environmental noise to structural damage characteristic information. Then variance dedication rate method is used to realize the dynamic fusion of multi-point vibration signals, extracting the vibration characteristic information of the overall structure accurately and comprehensively. Finally, VMD method is used to decompose the fused signals into many of intrinsic mode function(IMF) components, and then Hilbert transform is applied to each IMF to obtain the marginal spectrum of fused signals. In the proposed method, the damage sensitive index is defined based on VMD marginal spectrum and served as the damage feature vector of hydraulic structure, combining the damage sensitive index and Mahalanobis distance function to identify the damage of hydraulic structure. The proposed method is applied to study the vibration responses of cantilever beam model. The results show that the proposed method can effectively extract the damage characteristics and identify the operation conditions of hydraulic structure, which provides a basis for the safe operation of hydraulic structure.