Nonlocal Damage Mechanics for Quantification of Health for Piezoelectric Sensor

Damage localization in piezo-ceramic using ultrasonic waves excited by dual point contact excitation and detection scheme

A novel experimental technique based on point contact and Coulomb coupling is devised and optimized for ultrasonic imaging of bulk and guided waves propagation in piezo-ceramics. The Coulomb coupling technique exploits the coupling and transfer of electric field to mechanical vibrations by excitation of phonons. The point contact excitation and detection technique facilitates the spatial-temporal imaging of ultrasonic waves. The motivation of this research is the diagnosis and localization of surface cracks in the piezoelectric sensors and actuators. The underlying principle of the detection scheme is that any discontinuity on the surface causes high localization of electric gradient. The localized electric field at the defect boundaries enables then to behave as secondary passive ultrasonic sources resulting in strong back reflections. However, due to the interference between transmitted and reflected wave components from rigid boundaries and defect, the resolution on the localization of the damage is challenging. Therefore, an algorithm based on the two-dimensional spectral decomposition is utilized for selective suppression of the transmitted wave. The algorithm includes data transformation and vectorization in polar coordinates for efficient spectral decomposition. In the spectral domain, the complex wave component (phase and amplitude) are suppressed for the transmitted wave field. The reflected wave component in the spectral domain is retained and retrieved back using inverse spectral transformation. The algorithm is successful in retaining and exemplifying only the reflected wave sources arising from the strong scattering of ultrasonic waves from the surface and sub-surface defects. In summary, a novel experimental technique based on Coulomb coupling and spectral decomposition technique has been implemented for localization of surface defect in piezo-ceramic structures.

Classification of Micro-Damage in Piezoelectric Ceramics Using Machine Learning of Ultrasound Signals

Ultrasound based structural health monitoring of piezoelectric material is challenging if a damage changes at a microscale over time. Classifying geometrically similar damages with a difference in diameter as small as 100 μm is difficult using conventional sensing and signal analysis approaches. Here, we use an unconventional ultrasound sensing approach that collects information of the entire bulk of the material and investigate the applicability of machine learning approaches for classifying such similar defects. Our results show that appropriate feature design combined with simple k-nearest neighbor classifier can provide up to 98% classification accuracy even though conventional features for time-series data and a variety of classifiers cannot achieve close to 70% accuracy. The newly proposed hybrid feature, which combines frequency domain information in the form of power spectral density and time domain information in the form of sign of slope change, is a suitable feature for achieving the best classification accuracy on this challenging problem.