改进DBSCAN的自动工作模态分析方法

O32; 为解决随机子空间法在模态参数识别过程中自动性差、虚假模态难以识别剔除等问题,提出一种新的模态参数辨识方法.采用协方差驱动的随机子空间法(Covariance-driven stochastic identification,SSI-COV)识别系统的模态参数;根据软硬准则初步剔除虚假模态并绘制三维稳定图;对基于密度的带噪声的空间聚类算法(Density-based spatial clustering algorithm with noise,DBSCAN)进行改进,自动确定敏感参数ε,并对候选模态进行聚类分析;对每一簇类模态,计算模态质量评价准则(Modal quality ass...

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Published in南京航空航天大学学报 Vol. 56; no. 4; pp. 677 - 686
Main Authors 孙嘉宝, 康杰, 董自瑞, 季红侠, 罗杰, 刘晓腾
Format Journal Article
LanguageChinese
Published 南京航空航天大学航天学院,南京 211106%上海卫星装备研究所,上海 200240 01.08.2024
Subjects
虚假模态
DBSCAN algorithm
DBSCAN算法
3D stabilization diagram
operational modal(OMA)
工作模态分析
stochastic subspace identification
spurious mode
随机子空间法
三维稳定图
Online AccessGet full text
ISSN1005-2615
DOI10.16356/j.1005-2615.2024.04.010

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Abstract O32; 为解决随机子空间法在模态参数识别过程中自动性差、虚假模态难以识别剔除等问题,提出一种新的模态参数辨识方法.采用协方差驱动的随机子空间法(Covariance-driven stochastic identification,SSI-COV)识别系统的模态参数;根据软硬准则初步剔除虚假模态并绘制三维稳定图;对基于密度的带噪声的空间聚类算法(Density-based spatial clustering algorithm with noise,DBSCAN)进行改进,自动确定敏感参数ε,并对候选模态进行聚类分析;对每一簇类模态,计算模态质量评价准则(Modal quality assessment criterion,MQAC),制定筛选准则,自动剔除虚假模态并识别真实模态.利用本文方法对桁架结构、广州塔、Z24桥实例进行模态参数识别验证,结果表明该方法可实现典型工程结构的自动工作模态分析,可有效剔除非白噪声激励及测量噪声导致的虚假模态.
AbstractList O32; 为解决随机子空间法在模态参数识别过程中自动性差、虚假模态难以识别剔除等问题,提出一种新的模态参数辨识方法.采用协方差驱动的随机子空间法(Covariance-driven stochastic identification,SSI-COV)识别系统的模态参数;根据软硬准则初步剔除虚假模态并绘制三维稳定图;对基于密度的带噪声的空间聚类算法(Density-based spatial clustering algorithm with noise,DBSCAN)进行改进,自动确定敏感参数ε,并对候选模态进行聚类分析;对每一簇类模态,计算模态质量评价准则(Modal quality assessment criterion,MQAC),制定筛选准则,自动剔除虚假模态并识别真实模态.利用本文方法对桁架结构、广州塔、Z24桥实例进行模态参数识别验证,结果表明该方法可实现典型工程结构的自动工作模态分析,可有效剔除非白噪声激励及测量噪声导致的虚假模态.
Abstract_FL In order to solve the problems of poor automaticity and difficult identification and elimination of spurious modes by covariance-driven stochastic subspace method,a new modal parameter identification method is proposed.Firstly,the covariance-driven stochastic subspace method is used to identify the modal parameters of the system.Secondly,according to the soft and hard criteria,the spurious modes are preliminarily eliminated and the 3D stabilization diagram is drawn.Then,the density-based spatial clustering algorithm with noise(DBSCAN)is improved,the sensitive parameter ε is automatically determined,and the candidate modes are clustered and analyzed.For each cluster of modalities,the modal quality assessment criterion(MQAC)is calculated,and a screening standard is formulated to determine the true modes of the system.Finally,the proposed method is used to verify the modal parameter identification of truss structure,Guangzhou Tower and Z24 bridge examples,The results indicate that this method can achieve autonomous modal analysis of typical engineering structures and effectively eliminate false modes caused by non-white noise excitation and measurement noise.
Author 康杰
刘晓腾
孙嘉宝
董自瑞
罗杰
季红侠
AuthorAffiliation 南京航空航天大学航天学院,南京 211106%上海卫星装备研究所,上海 200240
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Author_FL JI Hongxia
LUO Jie
KANG Jie
LIU Xiaoteng
DONG Zirui
SUN Jiabao
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DocumentTitle_FL Improved DBSCAN for Automated Operational Modal Analysis Method
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Keywords 虚假模态
DBSCAN algorithm
DBSCAN算法
3D stabilization diagram
operational modal(OMA)
工作模态分析
stochastic subspace identification
spurious mode
随机子空间法
三维稳定图
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PublicationTitle 南京航空航天大学学报
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PublicationYear 2024
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Snippet O32; 为解决随机子空间法在模态参数识别过程中自动性差、虚假模态难以识别剔除等问题,提出一种新的模态参数辨识方法.采用协方差驱动的随机子空间法(Covariance-driven...
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