Dynamic system parameter identification by stochastic realization methods

Methods for modal parameter identification of random vibrating systems from multi-output data only are presented. These methods use a multivariate state-space model and exploit shift properties of a block Hankel matrix, formed from the covariance matrices of output data and shift properties of the observability matrix. Ordinary least squares, total least squares, and partial least squares algorithms are used to determine the transition matrix of the model, which contains all modal information of the vibrating system. A new iterative procedure is also developed to determine this transition matrix. These methods are compared using numerical examples based on simulations and experimental results.