北京航空航天大学学报 ›› 2015, Vol. 41 ›› Issue (5): 811-816.doi: 10.13700/j.bh.1001-5965.2014.0344

• 论文 • 上一篇    下一篇

基于模糊聚类的模态参数全因素自动验证方法

周思达, 周小陈, 刘莉, 杨武   

  1. 北京理工大学 宇航学院, 飞行器动力学与控制教育部重点实验室, 北京 100081
  • 收稿日期:2014-06-11 修回日期:2014-07-22 出版日期:2015-05-20 发布日期:2015-06-02
  • 通讯作者: 周思达(1984—),男,云南昆明人,讲师,zhousida@bit.edu.cn,主要研究方向为飞行器结构动力学、时变结构系统建模与辨识. E-mail:zhousida@bit.edu.cn
  • 基金资助:

    北京理工大学基础研究基金(20120142009)

Fuzzy-clustering-based all-factor automatous validation approach of modal parameters of structures

ZHOU Sida, ZHOU Xiaochen, LIU Li, YANG Wu   

  1. Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
  • Received:2014-06-11 Revised:2014-07-22 Online:2015-05-20 Published:2015-06-02

摘要:

为了解决实验模态分析中的模态参数验证问题,利用模糊聚类分析实现了模态参数的自动验证,降低了模态参数验证对使用者主观因素的依赖,并提高了实验模态分析工作中模态参数验证的效率.首先,将模态参数分为标量型和向量型;其次,采用传统的模糊聚类方法对标量型模态参数进行综合聚类分析;再次,为解决模糊聚类高维困难,提出了一个基于模态置信准则的距离函数,并以此完成模态振型的模糊聚类;然后,综合标量型和向量型模态参数模糊聚类的结果,实现模态参数的全因素自动验证;最后,通过实验结果对所提出的方法进行了验证,结果表明该方法能够自动、准确、高效地完成结构模态参数的验证.

关键词: 模态验证, 模糊聚类, 全因素, 振型聚类, 自动

Abstract:

To solve the problem of modal parameter validation, an automatous validation approach of modal parameters was realize by using the fuzzy clustering analysis, which reduced the dependence of users' subjective experience on modal parameter validation, and improve the efficiency of modal parameter validation at modal analysis work. First, the modal parameters are divided into the scalar type and the vector type. Second, the scalar modal parameters were clustered by the convention fuzzy clustering approach. Third, the modal shape were fuzzy clustered by using a new proposed modal assurance criterion based metric function to solve the high-dimensional difficulty of fuzzy clustering. Then, combining the clustering results both of the scalar and the vector modal parameters, the all-factor automatous validation of modal parameters was accomplished. Finally, the proposed approach was validated by experimental results and illustrate that the proposed approach can automatously, accurately and high-efficiently validate the modal parameters.

Key words: modal validation, fuzzy clustering, all-factor, clustering of mode shapes, autonomous

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