Time Domain Identification Method for Random Dynamic Loads and its Application on Reconstruction of Road Excitations

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.