Interval-oriented eigensystem realization algorithm and its modification for structural modal parameter identification with bounded uncertainties

IF 5.8 2区 工程技术 Q1 ACOUSTICS Journal of Sound and Vibration Pub Date : 2025-04-14 Epub Date: 2025-01-04 DOI:10.1016/j.jsv.2025.118929
Chen Yang , Xinhuan Xu , Xiaohan Wang , Ziyao Fan
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Abstract

Structural modal parameter identification is the initial step in modeling, monitoring and controlling dynamic systems, which can determine the accuracy of dynamics and control research. However, the uncertainty of dynamic systems is difficult to quantify, which will lead to deviations in structural modal parameter identification. Aiming to identify modal parameters under the influence of structural uncertainty parameters, this study proposed a novel interval-oriented eigensystem realization algorithm (ERA) and its modification with bounded uncertainties, which is particularly suitable for the case where structural uncertainty samples are scarce. The uncertain structures are quantified as interval uncertain parameters, which can reduce the need for quantification of uncertainty parameters without loss of accuracy. The first and second-order interval-oriented singular value decomposition (SVD) is developed, which is regarded as an important tool to solve the interval Hankel matrix. The conventional modal parameter identification method of ERA and ERA/DC are extended into the interval framework using first and second-order interval perturbation with a detailed derivation process, and the identified bounds of frequency and damping ratio can be accurately estimated using both interval-oriented ERA and ERA/DC in conjunction with first and second-order interval perturbation SVD. Finally, two numerical examples and one experimental verification are used to assess the proposed method.
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有界不确定结构模态参数辨识面向区间的特征系统实现算法及其改进
结构模态参数辨识是动力学系统建模、监测和控制的第一步,它决定了动力学和控制研究的准确性。然而,动力系统的不确定性难以量化,这将导致结构模态参数识别的偏差。针对结构不确定性参数影响下的模态参数识别问题,提出了一种新的面向区间的特征系统实现算法(ERA)及其有界不确定性修正算法,该算法特别适用于结构不确定性样本稀缺的情况。将不确定结构量化为区间不确定参数,在不影响精度的前提下减少了不确定参数量化的需要。提出了一阶和二阶面向区间的奇异值分解(SVD)方法,并将其作为求解区间汉克尔矩阵的重要工具。将传统的ERA和ERA/DC模态参数辨识方法推广到一阶和二阶区间摄动的区间框架中,并进行了详细的推导过程,利用面向区间的ERA和ERA/DC结合一阶和二阶区间摄动SVD可以准确估计识别出的频率和阻尼比边界。最后,用两个数值算例和一个实验验证对所提出的方法进行了验证。
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来源期刊
Journal of Sound and Vibration
Journal of Sound and Vibration 工程技术-工程:机械
CiteScore
9.10
自引率
10.60%
发文量
551
审稿时长
69 days
期刊介绍: The Journal of Sound and Vibration (JSV) is an independent journal devoted to the prompt publication of original papers, both theoretical and experimental, that provide new information on any aspect of sound or vibration. There is an emphasis on fundamental work that has potential for practical application. JSV was founded and operates on the premise that the subject of sound and vibration requires a journal that publishes papers of a high technical standard across the various subdisciplines, thus facilitating awareness of techniques and discoveries in one area that may be applicable in others.
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