Modal parameter identification under non-stationary ambient excitation based on continuous time AR model

Abstract

A new time-domain modal identification method of linear time-invariant system driven by the non- stationary Gaussian random excitation is introduced based on the continuous time AR model. The method can identify physical parameters of the system from response data. In order to identify the parameters of the system, the structural dynamic equation is first transformed into the continuous time AR model, and subsequently written into the forms of observation equation and state equation which is just a stochastic differential equation. Secondly, under the assumption that the uniformly modulated function is approximately equal to a constant matrix in a very short time period, the uniformly modulated function is identified piecewise. Then, we present the exact maximum likelihood estimators of parameters by virtue of the Girsanov theorem. Finally, the modal parameters are identified by eigenanalysis. Numerical results show that the method we introduce here not only has high precision and robustness, but also has very high computing efficiency. Therefore, it is suitable for real-time modal identification.