Aggelos G. Poulimenos

IDENTIFICATION AND MODEL UPDATING OF A NON-STATIONARY VIBRATING SYSTEM

Non-stationary systems, which are commonly encountered in many fields of science, are characterized by time-varying features and require time-frequency methods for their analysis. This study considers the problem of identification and model updating of a non-stationary vibrating system. In particular, a number of identification methods and a model updating procedure are evaluated and compared through application to a time-varying “bridge-like” laboratory structure. The identification approaches include Frequency Response Function based parameter estimation techniques, Subspace Identification and Functional Series modelling. All methods are applied to both output-only and input-out-put data. Model updating is based upon a theoretical model of the structure obtained using a Rayleigh-Ritz methodology, which is updated to account for time-dependence and nonlinearity via the identification results. Interesting comparisons, among both identification and model updating results, are performed. The results of the study demonstrate high modelling accuracy, illustrating the effectiveness of model updating techniques in non-stationary vibration modelling.Copyright

IDENTIFICATION OF TIME-VARYING STRUCTURES UNDER UNOBSERVABLE EXCITATION: AN OVERVIEW AND EXPERIMENTAL COMPARISON OF PARAMETRIC METHODS

This paper addresses the problem of parametric time-domain identification and dynamic analysis for time-varying mechanical structures under unobservable random excitation. The identification uses Time-dependent AutoRegressive Moving Average (TARMA) models (or state-space equivalents), which are conceptual extensions of their conventional (time-invariant) ARMA counterparts in that their parameters and innovations variance are varying with time. TARMA methods may be classified according to the type of mathematical structure imposed upon the evolution of the model parameters as follows: (a) Unstructured parameter evolution methods, (b) stochastic parameter evolution methods, and (c) deterministic parameter evolution methods [1]. The characteristics and relative merits of each class are outlined.