基于模态参数提取的随机子空间辨识算法改进

摘要/Abstract

摘要： 随机子空间辨识(SSI)算法在大型结构的振动检测、损伤识别中有着重要的作用。引入稀疏优化取代最小二乘法来获得尽可能稀疏的状态矩阵，引入Kmeans算法从众多模态参数中选出真实模态，以避免虚假模态的产生。实验结果表明，所构建的稀疏改进SSI算法能准确提取模态参数，对工程应用具有较大的参考价值。

关键词: 随机子空间辨识算法, 稀疏优化, 最小二乘法：模态参数：Kmeans算法

Abstract: SSI algorithm played an important role in the large structure vibration detection and damage identification. Sparse optimization solution was introduced to replace the least square method that was used to get sparser state matrix. Kmeans algorithm was introduced to elect real modal parameters from many modal parameters so as to eliminate the false modals effectively. The experimental results show that optimization solution of SSI algorithm may accurately extract modal parameters. The work herein has reference values in engineering applications.

Key words: stochastic subspace identification (SSI) algorithm, sparse optimization, least square method, modal parameter, Kmeans algorithm

中图分类号:

TP391.4

李玉刚, 叶庆卫, 周宇, 方宁. 基于模态参数提取的随机子空间辨识算法改进[J]. 中国机械工程.

LI Yugang, YE Qingwei, ZHOU Yu, FANG Ning. Improvement of SSI Algorithm Based on Extraction of Modal Parameters[J]. China Mechanical Engineering.

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