Application of frequency domain ARX models and extreme value statistics to damage detection

Abstract

In this study, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored. Damage sensitive features that explicitly consider the nonlinear system input/output relationships produced by damage are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS is useful in this case because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the current method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based method using piezoelectric (PZT) material as both an actuator and a sensor is then proposed as a possible solution to the problem of damage localization.