Degradation Performance Modeling and Optimal Maintenance Planning of Deteriorating Critical Infrastructures
Mingyang Li, Ph.D.
April 28, 2023
12 PM – 1 PM ET
The sheer deterioration of critical infrastructures with field degradation uncertainty, observational data scarcity, and complex interdependencies/dependencies calls for the development of advanced analytics methods to improve degradation modeling and maintenance optimization of in-service deteriorating infrastructures. In this talk, we will introduce both predictive and prescriptive analytics methods to improve the degradation performance modeling and cost-effective maintenance planning of the deteriorating critical infrastructures by addressing various data-driven modeling and decision analytics challenges. Case studies are also provided to illustrate the proposed methods and further demonstrate their improved performance (e.g., prediction accuracy/precision, cost reduction) over existing analytics-based methods.
Mingyang Li, Ph.D., is an associate professor in the Department of Industrial and Management Systems Engineering at the University of South Florida. He received his Ph.D. in systems and industrial engineering from the University of Arizona. His research interests focus on data analytics and informatics with diverse applications in reliability & quality, healthcare, energy, homeland security, manufacturing, etc. He develops and applies advanced statistical methods and computational tools, integrated with domain knowledge, to address problems (e.g., modeling, prediction, design, monitoring, diagnostics, prognostics, planning, scheduling, control, etc.) in a complex data environment. His research has been published in high-quality journals, such as IISE Transactions, IEEE Transactions on Reliability, Reliability Engineering & System Safety, and the Journal of Quality Technology and Quality Engineering. View website.
