Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements
Document Type
Conference Proceeding
Publication Date
4-18-2023
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
Fault parameters in a structure are identified by matching measurements with model predictions in the parametric space. As high frequency measurements are preferred to uncover small-sized damage, piezoelectric impedance/admittance active interrogation has shown promising aspects. Nevertheless, challenges remain. The amount of useful measurement information is generally insufficient to pinpoint damage. The inverse identification is usually underdetermined. In this research, we develop a combinatorial enhancement to tackle these challenges. A tunable piezoelectric impedance sensing procedure is developed in which an adaptive inductor element is integrated with the piezoelectric transducer, which will lead to significantly enriched measurement data for the same damage. Subsequently, an intelligent learning automata-based multi-objective particle swarm optimization framework is synthesized to inversely identify the damage location and severity. Case studies are conducted to highlight the accuracy of the damage identification.
Publication Title
Proceedings Volume 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023
Recommended Citation
Zhang, Y.,
Dupont, J.,
Wang, T.,
Zhou, K.,
&
Tang, J.
(2023).
Structural damage identification using piezoelectric impedance sensing with enhanced optimization and enriched measurements.
Proceedings Volume 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023.
http://doi.org/10.1117/12.2658628
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17335