ME Research and Regional ME Seminar Series: Structural Dynamics Identification via Computer Vision and Data Analytics in Structural Health Monitoring

Wednesday, February 13, 2019
10:00 am to 10:50 am
Floor/Room #: 
218

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WPI ME Research Seminar Series 2018-2019
Regional ME Seminar Series 2018-2019


Structural Dynamics Identification via Computer Vision and Data Analytics in Structural Health Monitoring

Professor Zhu Mao
Department of Mechanical Engineering
University of Massachusetts Lowell
 

10:00-10:50 am, Wednesday, 2/13/2019
Higgins Labs 218


Abstract

Among the current efforts to improve structural safety and reliability, Structural Health Monitoring (SHM) plays a very important roll, in which real-time data are acquired and analyzed for the ultimate goal of status awareness, condition-based maintenance and retrofit, operational risk minimization, and human life safety. Yet any such condition assessment is inevitably constrained by the observability of the sensing hardware and corrupted by various kinds of uncertainties, which degrades the SHM decision-making capability dramatically, leading to unidentifiable system dynamics, as well as both false damage alarms and missed detections or classifications. In this presentation, the research activities at UMass Lowell related to non-contact full-field system testing and identification will be introduced. Specifically, the enhanced modal identification and damage detection on wind turbine blades, by means of video motion magnification and other computer vision techniques, will be presented. The second half the presentation concerns the uncertainty quantification, and its applications to the SHM decisions. Probabilistic uncertainty quantification models which characterize the actual distributions of selected SHM features as random variables will be briefed. Structural damage is detected via outlier analysis based on the established probabilistic model, given the quantified significance level of decision threshold. Receiver operating characteristics will be compared among different scenarios to have an optimal trade-off between sensitivity and specificity. Bayesian decision theory is then adopted to classify the type of damages in a recursive manner, and posterior probability facilitates the selection of the correct model.


About the Speaker

mao zhu

Zhu Mao received his Ph.D. in 2012 from the University of California San Diego, and now holds the Assistant Professor position in the Department of Mechanical Engineering at the University of Massachusetts Lowell.  His research interests include dynamics and vibration, structural health monitoring, signal processing, statistical modeling and risk analysis, uncertainty quantification and prognosis.

Dr. Mao has published over 60 papers on top tier journals and internationally-recognized conference proceedings.  He serves as the reviewers for 28 international journals and is currently the Chair of the Technical Division of Model Validation and Uncertainty Quantification at the Society for Experimental Mechanics (SEM). Dr. Mao’s research has been funded by the National Science Foundation, Department of Defense, Department of Energy, as well as industry sponsors.  He is the winner of 2011 D. J. DeMichele Scholarship Award of SEM, the recipient of 2018 AFOSR Young Investigator Program award, and the SAGE Publishing Young Engineer Lecture Award presented by SEM in 2019.

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