Scholarlink

<p>To address the challenges of poor parameter identification performance in environments characterized by strong noise and dense modal signals, this study proposes a novel structural modal parameter identification method based on complete modal decomposition. This method integrates variational mode decomposition (VMD), singular value decomposition (SVD), and the grey wolf optimization (GWO) algorithm to achieve accurate identification of modal parameters under such adverse conditions. The effectiveness of the proposed method is validated through the analysis of experimental vibration response signals from a gantry crane prototype and numerical simulations of a cantilever beam. The results demonstrate that the complete modal decomposition parameter identification method, which combines time-frequency domain analysis with optimization algorithms, exhibits high accuracy in identifying dense modal parameters in environments with strong interference.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A method for solving structural modal parameters based on complete modal decomposition

  • 期刊论文

  • 发表日期：

    2026年1月10日

查看全文
  • Shilong Zeng,
  • Hongyuan Du

摘要

To address the challenges of poor parameter identification performance in environments characterized by strong noise and dense modal signals, this study proposes a novel structural modal parameter identification method based on complete modal decomposition. This method integrates variational mode decomposition (VMD), singular value decomposition (SVD), and the grey wolf optimization (GWO) algorithm to achieve accurate identification of modal parameters under such adverse conditions. The effectiveness of the proposed method is validated through the analysis of experimental vibration response signals from a gantry crane prototype and numerical simulations of a cantilever beam. The results demonstrate that the complete modal decomposition parameter identification method, which combines time-frequency domain analysis with optimization algorithms, exhibits high accuracy in identifying dense modal parameters in environments with strong interference.