ECE Departmental Seminar
Structural Health Monitoring with Novel Self-Powered Sensing Systems using Machine Learning Frameworks
Prof. Rigoberto Burgueño
Department of Civil Engineering, Stony Brook University
Friday, 4/23/21, 1:00pm
To obtain access the Zoom link for this seminar, please click here to register.
Abstract: One of the challenges in structural health monitoring is the power required for sensors to collect and communicate data. Self-powered sensors are able to harvest power from their environment, i.e., strain and vibration of the host structure. However, the harvested power with current technology is still limited, and improving the system’s efficiency requires reducing the power budget. A way to minimize the communication power demand is to transmit the minimum amount of information, namely one bit. The binary signal can be generated at a sensor node according to a local rule based on physical measurements, but interpretation at the global level requires dealing with time-delayed and incomplete/missing binary data, which implies system information with reduced resolution. This research addressed the noted issues through the development of data interpretation frameworks for use in energy-lean SHM of plate-like structures. Data interpretation frameworks for data-driven SHM with discrete time-delayed binary and incomplete (sparse) data were established based on the integration of machine learning, pattern recognition, a data fusion model, probabilistic, and statistical approaches. Finite element simulations on an aircraft stabilizer wing and structural plates were conducted to validate the proposed methodology. Further, experimental vibration tests on dynamically loaded plates were carried out to demonstrate the applicability of the approach on a realistic structure. Results indicate that the proposed data interpretation frameworks employing machine learning can be used as damage identification algorithms in energy-lean novel self-powered sensor networks.
Bio: Dr. Rigoberto Burgueño is professor and chair of the Department of Civil Engineering at Stony Brook University. Prior to joining Stony Brook in fall 2018 he was a professor in the Department of Civil and Environmental Engineering at Michigan State University. He received his Ph.D. degree in engineering sciences with a specialization in structural engineering from the University of California, San Diego. His research activities include: harnessing elastic instabilities for smart/adaptive materials and structures, artificial intelligence methods for structural health monitoring; composite materials and structures, experimental characterization of materials and structures, and earthquake engineering. His research has been funded by the National Science Foundation, the Federal Highway Administration, the Michigan Department of Transportation, and the Precast/Prestressed Concrete Institute. Dr. Burgueño has been recipient of several teaching and research awards, is active in the technical activities of the American Concrete Institute, and is an editorial board member of the Journal of Composites for Construction.