Show simple item record

dc.contributor.authorAlmaktoom, Abdulaziz T.
dc.contributor.authorWang, Zequn
dc.contributor.authorWang, Pingfeng
dc.date.accessioned2015-10-30T19:52:11Z
dc.date.available2015-10-30T19:52:11Z
dc.date.issued2014
dc.identifier.citationAlmaktoom, Abdulaziz T.; Wang, Zequn; Wang, Pingfeng. 2014. Probabilistic design of smart sensing functions for structural health monitoring and prognosis. ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 3A: 39th Design Automation Conference Portland, Oregon, USA, August 4–7, 2013en_US
dc.identifier.isbn978-0-7918-5588-1
dc.identifier.otherWOS:000362380000047
dc.identifier.urihttp://dx.doi.org/10.1115/DETC2013-12598
dc.identifier.urihttp://hdl.handle.net/10057/11572
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractSignificant technological advances in sensing and communication promote the use of large sensor networks to monitor structural systems, identify damages, and quantify damage levels. Prognostics and health management (PHM) technique has been developed and applied for a variety of safety-critical engineering structures, given the critical needs of the structure health state awareness. The PHM performance highly relies on real-time sensory signals which convey the structural health relevant information. Designing an optimal structural sensor network (SN) with high detectability is thus of great importance to the PHM performance. This paper proposes a generic SN design framework using a detectability measure while accounting for uncertainties in material properties and geometric tolerances. Detectability is defined to quantify the performance of a given SN. Then, detectability analysis will be developed based on structural simulations and health state classification. Finally, the generic SN design framework can be formulated as a mixed integer nonlinear programming (MINLP) using the detectability measure and genetic algorithms (GAs) will be employed to solve the SN design optimization problem. A power transformer study will be used to demonstrate the feasibility of the proposed generic SN design methodology.en_US
dc.language.isoen_USen_US
dc.publisherAmerican Society of Mechanical Engineersen_US
dc.relation.ispartofseriesASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference;v.3A
dc.subjectDesignen_US
dc.subjectStructural health monitoringen_US
dc.titleProbabilistic design of smart sensing functions for structural health monitoring and prognosisen_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2013 by ASMEen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record