A new approach for time-varying (TV) modal parameters identification is proposed in this research. In the identification process, the entire signal is divided into successive short time windows, where the structure response under white noise excitation is transformed into modal coordinates by the Independent Component Analysis (ICA) method. The decoupled time-varying Auto-Regressive Moving-Average (ARMA) model based on the new assumption of “short-time linearly varying” (STLV) is established with the extracted modal coordinates. The TV parameters can be obtained by solving a nonlinear least squares problem. The main motivation of the proposed approach is to simplify the model and reduce the computational difficulty. The effectiveness and accuracy are validated via both the numerical example and experiment.

提出了一种时变（TV）模态参数识别的新方法。在识别过程中，整个信号被分成连续的短时窗，在此期间，通过独立分量分析（ICA）方法将白噪声激发下的结构响应转换为模态坐标。利用提取的模态坐标，建立了基于“短时线性变化”（STLV）的新假设的解耦时变自回归移动平均（ARMA）模型。TV参数可以通过解决非线性最小二乘问题获得。提出的方法的主要动机是简化模型并降低计算难度。通过数值例子和实验验证了有效性和准确性。