Electromechanical impedance-based damage identification enhancement using bistable and adaptive piezoelectric circuitry

The electromechanical impedance-based damage identification approaches have shown excellent potential in identifying small-sized structural defects, while maintaining simplicity in implementation. The available independent impedance measurement data sets, however, are generally far fewer than the number of required system parameters. As a result, the inverse problem for damage identification is seriously underdetermined, which undermines the reliability of damage prediction since the inverse solution becomes extremely sensitive to even small amount of error in the measurement, especially in practical applications with unavoidable noise and damping influences. This research aims to advance the state of the art by developing a novel approach that (a) enables highly accurate measurement of damage-induced impedance variations against noise and (b) fundamentally improves the underdetermined inverse problem to reliably identify the location and severity of small damages. This new approach utilizes the strongly non-linear bifurcation phenomena in bistable electrical circuits that may exhibit dramatic changes in the response due to small input variations. In this study, an array of bistable circuits is strategically integrated with the structure and piezoelectric transducer so that small damage-induced changes in the piezoelectric impedance can be accurately determined by monitoring whether each circuit exhibits intra- or inter-well responses. This measurement data set is greatly enriched by utilizing an adaptive piezoelectric circuitry with tunable inductor integrated with the monitored structure, which introduces more degrees of freedom into the system. By selectively tuning the inductance values, the dynamic characteristic of the electromechanically coupled system can be altered, thereby one can significantly increase the number of impedance variation measurements for the same damage profile. The enriched data set is utilized to fundamentally improve the underdetermined inverse problem for damage identification. A series of numerical and experimental damage identification studies verify that the proposed methodology can significantly enhance the accuracy and reliability of impedance-based damage identification.