A time domain method for identifying random dynamic loads is proposed based on spectral decomposition and regularization, which to some extent makes up for the deficiency of frequency domain methods. The random dynamic loads are descripted with their time domain mean functions and covariance matrix, which can intuitively reflect the statistical characteristics of the loads. Therein the random dynamic load identification is transformed into the load mean function identification and covariance matrix reconstruction. The forward identification models are mainly established based on Green’s kernel function method, and then spectral decomposition is conducted to transform the identification of load covariance matrix into a series of identifications of eigenvectors. To overcome the ill-posedness in the inverse process, the least-square QR iterative regularization is adopted. Two numerical examples and an application on the reconstruction of road excitations acting on a vehicle are studied to verify the effectiveness of the proposed method.

中文翻译：

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随机动荷载时域辨识方法及其在道路激励重建中的应用

提出了一种基于谱分解和正则化的随机动态载荷时域识别方法，在一定程度上弥补了频域方法的不足。用时域均值函数和协方差矩阵对随机动态载荷进行描述，可以直观地反映载荷的统计特性。其中随机动载荷识别转化为载荷均值函数识别和协方差矩阵重构。前向辨识模型主要基于格林核函数法建立，然后进行谱分解，将负荷协方差矩阵的辨识转化为一系列特征向量的辨识。为了克服逆过程中的不适定性，采用最小二乘QR迭代正则化。研究了两个数值例子和一个在车辆上的道路激励重建的应用，以验证所提方法的有效性。