基于随机子空间法的模态参数自动提取

doi:10.3901/JME.2018.09.187

机械工程学报 ›› 2018, Vol. 54 ›› Issue (9): 187-194.doi: 10.3901/JME.2018.09.187

基于随机子空间法的模态参数自动提取

张永祥¹, 刘心^2,3, 褚志刚², 黄迪², 王光建²

1. 重庆大学航空航天学院重庆 400044;
2. 重庆大学汽车工程学院重庆 400044;
3. 中国第一汽车股份有限公司研发总院长春 130011

收稿日期:2017-07-13 修回日期:2018-02-12 出版日期:2018-05-05 发布日期:2018-05-05
通讯作者: 褚志刚(通信作者),男,1978年出生,博士,教授。主要研究方向为振动噪声测量分析技术,数字信号处理,噪声源识别技术。E-mail:zgchu@cqu.edu.cn
作者简介:张永祥,男,1978年出生,博士,副教授。主要研究方向为结构动力学和计算流体力学。E-mail:zhangyx@cqu.edu.cn;刘心,女,1991年出生,硕士。主要研究方向为结构模态分析技术理论及应用研究。E-mail:501292505@qq.com;黄迪,男,1993年出生,硕士。主要研究方向为结构模态分析技术理论及应用研究。E-mail:dhuang@cqu.edu.com;王光建,男,1973年出生,博士,教授。主要研究方向为机械系统动力学。E-mail:gjwang@cqu.edu.cn
基金资助:
重庆市重大应用开发计划（cstc2015yykfc60003）和中央高校基本科研业务费（106112017CDJQJ338810，CDJXZ2016003）资助项目。

Autonomous Modal Parameter Extraction Based on Stochastic Subspace Identification

ZHANG Yongxiang¹, LIU Xin^2,3, CHU Zhigang², HUANG Di², WANG Guangjian²

1. College of Aerospace Engineering, Chongqing University, Chongqing 400044;
2. College of Automotive Engineering, Chongqing University, Chongqing 400044;
3. General Research Institute, China FAW Corporation Limited, Changchun 130011

Received:2017-07-13 Revised:2018-02-12 Online:2018-05-05 Published:2018-05-05

摘要/Abstract

摘要： 为了自动且有效地剔除虚假模态、避免人为参与模态模型定阶及模态拾取，进而实现模态参数的自动准确识别，提出了一种新型的随机子空间法运行模态参数自动识别方法。该方法首先构造2个不同维度的汉克尔矩阵对应的随机子空间法运行模态识别模型并求解其极点，在此基础上针对2个模型的同阶极点进行匹配进而获得超清的稳态图，再对该稳态图进行谱系聚类分析，最终自动准确识别出模态参数。5自由度质量-弹簧-阻尼仿真系统及矩形平板的运行模态试验识别结果均验证了方法的有效性。

关键词: 模态参数, 随机子空间法, 稳态图, 运行模态分析, 自动提取

Abstract: To remove spurious modes automatically and effectively, to avoid artificial participation in modal model order determination and modal pickup, so as to extract the modal parameter automatically and exactly, a novel autonomous modal parameter extraction method based on stochastic subspace identification method is proposed. Firstly, two stochastic subspace models with different dimension Hankel matrices for operating modal parameter identification are constructed and their poles are computed. On this basis, a crystal stabilization diagram is obtained by matching the same order poles of the two models. Finally, hierarchical clustering analysis is performed to the stabilization diagram and then exact modal parameters are automatically extracted. The effectiveness of the proposed method is verified by the simulation of operational modal analysis for a 5DOF mass-spring-damping system and the experiment for a rectangle plate.

Key words: automatic extraction, modal parameter, operational modal analysis, stabilization diagram, stochastic subspace identification

中图分类号:

TB123

张永祥, 刘心, 褚志刚, 黄迪, 王光建. 基于随机子空间法的模态参数自动提取[J]. 机械工程学报, 2018, 54(9): 187-194.

ZHANG Yongxiang, LIU Xin, CHU Zhigang, HUANG Di, WANG Guangjian. Autonomous Modal Parameter Extraction Based on Stochastic Subspace Identification[J]. Journal of Mechanical Engineering, 2018, 54(9): 187-194.

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基于随机子空间法的模态参数自动提取

Autonomous Modal Parameter Extraction Based on Stochastic Subspace Identification

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