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dc.contributor.authorWang, Pingfeng
dc.contributor.authorYoun, Byeng D.
dc.contributor.authorHu, Chao
dc.contributor.authorHa, Jong Moon
dc.contributor.authorJeon, Byungchul
dc.date.accessioned2015-06-11T19:27:05Z
dc.date.available2015-06-11T19:27:05Z
dc.date.issued2015-06
dc.identifier.citationWang, Pingfeng; Youn, Byeng D.; Hu, Chao; Ha, Jong Moon; Jeon, Byungchul. 2015. A probabilistic detectability-based sensor network design method for system health monitoring and prognostics. Journal of Intelligent Material Systems and Structures, June 2015:vol. 26:no. 9:pp 1079-1090en_US
dc.identifier.issn1045-389X
dc.identifier.otherWOS:000354856600006
dc.identifier.urihttp://dx.doi.org/10.1177/1045389X14541496
dc.identifier.urihttp://hdl.handle.net/10057/11293
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractSignificant technological advances in sensing promote the use of large sensor networks to monitor engineered systems, identify damages, and quantify damage levels. Prognostics and health management technique has been developed and applied for a variety of safety-critical engineered systems, given the critical needs of system health state awareness. The prognostics and health management performance highly relies on real-time sensory signals that convey system health-relevant information. Designing an optimal sensor network with high detectability of system health state is thus of great importance to the prognostics and health management performance. This article proposes a generic sensor network design framework using a detectability measure while accounting for uncertainties in material properties and geometric tolerances. Our contributions in this article are threefold: (1) the definition of a detectability measure to quantify the diagnostic/prognostic performance of a given sensor network, (2) the development of detectability analysis based on physics-based simulation and health state classification, and (3) the formulation of a generic sensor network design optimization problem as a mixed integer nonlinear programming. We employ the genetic algorithms to solve the sensor network design optimization problem. The merit of the proposed methodology is demonstrated with a power transformer system, which suffers from core and winding joint loosening due to consistent vibration.en_US
dc.description.sponsorshipThis work was partially supported by a grant from the Energy Technology Development Program (2010101010027B) and International Collaborative R&D Program (0420-2011-0161) of Korea Institute of Energy Technology Evaluation and Planning (KETEP), funded by the Korean government's Ministry of Trade, Industry and Energy the Basic Research Project of Korea Institute of Machinery and Materials (Project Code: SC0830) supported by a grant from Korea Research Council for Industrial Science & Technology; and the Institute of Advanced Machinery and Design at Seoul National University (SNU IAMD).en_US
dc.language.isoen_USen_US
dc.publisherSAGE Publicationsen_US
dc.relation.ispartofseriesJournal of Intelligent Material Systems and Structures;v.26:no.9
dc.subjectStructural health monitoringen_US
dc.subjectOptimizationen_US
dc.subjectEmbedded intelligenceen_US
dc.titleA probabilistic detectability-based sensor network design method for system health monitoring and prognosticsen_US
dc.typeArticleen_US
dc.rights.holderCopyright © 2015 by SAGE Publications


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