Liu Xiaoteng, Dong Zirui, Ji Hongxia, Yue Zhenjiang, Kang Jie
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Shanghai Institute of Spacecraft Equipment, Shanghai 201109, China.
Sensors (Basel). 2025 Jul 14;25(14):4397. doi: 10.3390/s25144397.
The structural modal parameter identification method can be classified into time-domain and frequency-domain methods. Practically, two types of methods are characterized by different advantages, and the estimated modal parameters are always subjected to statistical uncertainties due to measurement noise. In this work, an uncertainty-based fusion method for structural mode identification is proposed to merge the advantages of different methods. The extensively applied time-domain AutoRegressive (AR) and frequency-domain Left-Matrix Fraction (LMF) models are expressed in a unified parametric model. With this unified model, a generalized framework is developed to identify the modal parameters of structures and compute variances associated with modal parameter estimates. The final modal parameter estimates are computed as the inverse-variance weighted sum of the results identified from different methods. A numerical and an experimental example demonstrate that the proposed method can obtain reliable modal parameter estimates, substantially mitigating the occurrence of extremely large estimation errors. Furthermore, the fusion method demonstrates enhanced identification capabilities, effectively reducing the likelihood of missing structural modes.
结构模态参数识别方法可分为时域方法和频域方法。实际上，这两种方法具有不同的优点，并且由于测量噪声，估计的模态参数总是存在统计不确定性。在这项工作中，提出了一种基于不确定性的结构模态识别融合方法，以融合不同方法的优点。广泛应用的时域自回归（AR）模型和频域左矩阵分式（LMF）模型被表示在一个统一的参数模型中。利用这个统一模型，开发了一个广义框架来识别结构的模态参数并计算与模态参数估计相关的方差。最终的模态参数估计值被计算为从不同方法识别出的结果的逆方差加权和。一个数值例子和一个实验例子表明，所提出的方法可以获得可靠的模态参数估计值，大大减少了极大估计误差的出现。此外，融合方法显示出增强的识别能力，有效地降低了遗漏结构模态的可能性。
