Application of ARMAV models to the identification and damagedetection of mechanical and civil engineering structures

Abstract

In this paper, the application of auto-regressive moving averagevector models to system identification and damage detection isinvestigated. These parametric models have already been applied for theanalysis of multiple input-output systems under ambient excitation. Theirmain advantage consists in the capability of extracting modal parametersfrom the recorded time signals, without the requirement of excitationmeasurement. The excitation is supposed to be a stationary Gaussian whitenoise. The method also allows the estimation of modal parameteruncertainties. On the basis of these uncertainties, a statistically baseddamage detection scheme is performed and it becomes possible to assesswhether changes of modal parameters are caused by, e.g. some damage orsimply by estimation inaccuracies. The paper reports first an example ofidentification and damage detection applied to a simulated system underrandom excitation. The `Steel-Quake' benchmark proposed in the framework ofCOST Action F3 `Structural Dynamics' is also analysed. This structure wasdefined by the Joint Research Centre in Ispra (Italy) to test steelbuilding performance during earthquakes. The proposed method gives anexcellent identification of frequencies and mode shapes, while dampingratios are estimated with less accuracy.