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I-UCRC: The University of Texas at Austin Site of the I-UCRC on Intelligent Maintenance Systems

$335,996FY2013CSENSF

University Of Texas At Austin, Austin TX

Investigators

Abstract

The University of Texas at Austin site for the I/UCRC for Intelligent Maintenance Systems (IMS) intends to pursue and understand methods addressing the scientific and practical challenges of condition-based and predictive maintenance of complex engineering systems. The site plans to expand the on-going IMS research with new ideas and foci including predictive maintenance in semiconductor manufacturing, vehicle technologies, oil industry, renewable energy, power plants and biomedicine. The University of Texas at Austin site intends to complement existing IMS efforts through focus on new approaches to diagnostics and prognostics in complex engineering systems, utilization of advanced diagnostics and predictive maintenance methods for monitoring of systems in a human body, and maintenance decision-making in distributed systems, with limited availability of distributed resources and uncertain supply and demand characteristics. It is estimated that the US economy spends more than $1 trillion for maintenance of critical assets, with more than 33% of these costs being wasted on ineffective maintenance. Optimization of these maintenance systems has the potential to greatly improve overall industrial productivity. The University of Texas at Austin Site of IMS intends to extend the research outcomes it brings to the center to new industries that have formerly not benefited from predictive maintenance research. The site plans to broadly disseminate research results through regular meetings with industrial partners, short courses on diagnostics and maintenance open to industry and academe, regular participation and publication in relevant conferences, and partnerships with universities in the US and abroad. They intend to actively promote diversity among students and researchers by leveraging the tremendous infrastructure UT offers in this regard.

View original record on NSF Award Search →