Stochastic parametric system identification approach for validation of finite element models: industrial applicationsAzer A. Kasimzade, Sertac Tuhta (pp. 41-61)
Stochastic parametric system parameters identification approach with taking into account the aliasing problem for validation of finite element models is presented. Investigated measurement noise perturbation infiuences to the identified system modal and physical parameters. Estimated measurement noise border, for which identified system parameters are acceptable for validation of finite element model of examine system. System identification is realized by observer Kalman filter and Subspace algorithms. In special case observer gain may be coincide with the Kalman gain. Stochastic state-space model of the structure are simulated by Monte-Carlo method.
stochastic systems identification, finite element, validation models, Monte-Carlo simulation, stochastic models.