改进DBSCAN的自动工作模态分析方法
Improved DBSCAN for Automated Operational Modal Analysis Method
孙嘉宝 1康杰 1董自瑞 2季红侠 2罗杰 1刘晓腾1
作者信息
- 1. 南京航空航天大学航天学院,南京 211106
- 2. 上海卫星装备研究所,上海 200240
- 折叠
摘要
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
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.
关键词
工作模态分析/随机子空间法/三维稳定图/虚假模态/DBSCAN算法Key words
operational modal(OMA)/stochastic subspace identification/3D stabilization diagram/spurious mode/DBSCAN algorithm引用本文复制引用
基金项目
航空航天结构力学及控制全国重点实验室青年学生项目(MCAS-S-0224G04)
国家自然科学基金青年基金(12102178)
南京航空航天大学新教师工作启动基金(YAH20137)
出版年
2024
国家科技期刊平台
NSTL
万方数据