Detection of Structural Cracks Using Piezoelectric Active Sensors

This study proposes a novel approach to identifying crack damage common to metallic structural elements under extreme cyclic loading. Using two piezoelectric pads surface mounted to a structural element, broadband signals can be used to locally excite the element using one pad and the corresponding system response measured with a second pad. An auto-regressive with exogenous input (ARX) system identification model is then fit to the collected system input-output response data. Complex roots (poles) of the ARX model’s characteristic equation are sensitive to structural damage causing a change in their location on the complex plane. To quantitatively identify the migration of poles in the complex plane, bivariate normal density functions are fit to poles of the same response mode observed during repeated excitation of the element. Certain pole clusters are shown to be more sensitive to damage resulting in statistically significant changes in the fitted normal density function for that cluster. Using the mean of the normal density function, the migration of ARX model poles upon the complex plane are shown to also be well correlated to damage severity. To illustrate the efficacy of the method, a simple aluminum plate with a cut at its midsection is monitored using piezoelectric active sensors commanded by a wireless active sensing unit.