This paper presents the use of statistically rigorous algorithms combined with active-sensing impedance methods for damage identification in engineering systems. In particular, we propose to use statistical pattern recognition methods to address damage classification and data mining issues associated with the examination of large numbers of impedance signals for health monitoring applications. The impedance-based structural health monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of damage identification applications. Relying on high frequency local excitations (typically > 30 kHz), this technique is very sensitive to minor changes in structural integrity in the near field of piezoelectric sensors. In this study, in order to diagnosis damage with levels of statistical confidence, the impedance-based monitoring is cast in the context of an outlier detection framework. A modified autoregressive model with exogenous inputs (ARX) in the frequency domain is developed. The damage sensitive feature is then computed by differentiating 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 (EVS) are employed to develop a robust damage classifier. By incorporating EVS, we establish a rigorous impedance-based health monitoring algorithm, which is able to provide structural systems with self-contained and self-diagnostic components. 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.

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