A Novel Method of Pure Output Modal Identification Based on Multivariate Variational Mode Decomposition

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Tao Li, Rui Hou, Kangkang Zheng, Lingfeng Li, Bo Liu
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引用次数: 0

Abstract

This paper proposes a novel parameterized frequency-domain modal parameter identification method, called direct modal variational mode decomposition (DMVMD), based on the multivariate variational mode decomposition (MVMD) framework and the principle of modal superposition. Under the constraint of normalized mode shapes, this paper theoretically derives the relationship between multivariate variational mode decomposition and the natural frequencies and mode shapes of structural systems. The aim is to extract K response modes and their corresponding mode shapes from the excited C-dimensional vibration signals of the measured component’s response. First, the measured multichannel vibration signals are decomposed into IMFs aligned with K-order natural frequencies using multivariate variational mode decomposition (MVMD). Then, the Hilbert equations and mode shape normalization constraints are used to solve the structural natural frequencies and mode shapes. Furthermore, the proposed multimodal identification algorithm has been validated through numerical simulations and experimental examples, demonstrating its high accuracy and robustness in modal identification. Compared to the existing multimodal algorithms related to variational mode decomposition, the proposed method is more direct and elegant. This method has been successfully applied to the modal parameter identification of subway tunnel structures, enabling accurate determination of the location of tunnel damage through analysis of the identified modal parameters.

Abstract Image

基于多变量模式分解的纯输出模态识别新方法
本文基于多元变分模态分解(MVMD)框架和模态叠加原理,提出了一种新颖的参数化频域模态参数识别方法,即直接模态变分模态分解(DMVMD)。在归一化模态振型的约束下,本文从理论上推导出多元变分模态分解与结构系统固有频率和模态振型之间的关系。目的是从被测构件响应的 C 维激励振动信号中提取 K 个响应模态及其相应的模态振型。首先,使用多变量变异模态分解(MVMD)将测量到的多通道振动信号分解为与 K 阶固有频率一致的 IMF。然后,利用希尔伯特方程和模态振型归一化约束求解结构固有频率和模态振型。此外,所提出的多模态识别算法还通过数值模拟和实验实例进行了验证,证明了其在模态识别方面的高精度和鲁棒性。与现有的与变分模态分解相关的多模态算法相比,所提出的方法更直接、更优雅。该方法已成功应用于地铁隧道结构的模态参数识别,通过对识别出的模态参数进行分析,可准确确定隧道损伤位置。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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