Time-varying parameter identification is essential for structural health monitoring and performance evaluation. In this paper, a combined method based on the variational mode decomposition and generalized Morse wavelet is proposed to identify the structural time-varying parameters. Based on the sparse property of structural response signals in wavelet domain, a fast iterative shrinkage-thresholding algorithm is adopted to reduce the noise. Then the de-noised signal is decomposed into multi- modes by the variational mode decomposition, and the generalized Morse wavelet is performed to identify the instantaneous frequency. To validate the proposed method, a numerical example including different frequency variations is studied. Experimental validations of a moving vehicle across a bridge and a time-varying cable system considering two patterns of cable tension variations in the laboratory are carried out to investigate the capability of the proposed approach. It is confirmed that the proposed approach can effectively perform the signal decomposition, while identifying the instantaneous frequencies of the time-varying systems accurately.

中文翻译：

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基于变分模态分解和广义莫尔斯小波的时变参数识别组合方法

时变参数识别对于结构健康监测和性能评估至关重要。本文提出了一种基于变分模态分解和广义Morse小波的组合方法来识别结构时变参数。基于小波域结构响应信号的稀疏特性，采用快速迭代收缩阈值算法来降低噪声。然后通过变分模态分解将去噪信号分解为多模态，并进行广义莫尔斯小波识别瞬时频率。为了验证所提出的方法，研究了一个包括不同频率变化的数值示例。考虑到实验室中两种电缆张力变化模式，对跨过桥梁的移动车辆和时变电缆系统进行了实验验证，以研究所提出方法的能力。经证实，该方法可以有效地进行信号分解，同时准确识别时变系统的瞬时频率。