An outlier analysis framework for impedance-based structural health monitoring

In this paper, in order to diagnose damage with statistical confidence, the impedance-based monitoring is cast in the context of an outlier detection framework. A modified auto-regressive model with exogenous inputs (ARX) in the frequency domain is developed and the damage-sensitive feature is obtained by quantifying the differences between the measured impedance and the output of the ARX model. Furthermore, because of the non-Gaussian nature of the feature distribution tails, extreme value statistics is employed to develop a robust damage classifier. This paper concludes with a numerical example on a 5 degree-of-freedom system and an experimental investigation on a multi-story building model to demonstrate the performance of the proposed concept.