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dc.contributor.authorTamilselvan, Prasanna
dc.contributor.authorWang, Pingfeng
dc.contributor.authorHu, Chao
dc.date.accessioned2015-10-30T19:03:37Z
dc.date.available2015-10-30T19:03:37Z
dc.date.issued2014
dc.identifier.citationTamilselvan, Prasanna; Wang, Pingfeng; Hu, Chao. 2014. Design of a robust classification fusion platform for structural health diagnostics. 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:000362380000037
dc.identifier.urihttp://dx.doi.org/10.1115/DETC2013-12601
dc.identifier.urihttp://hdl.handle.net/10057/11568
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractEfficient health diagnostics provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of engineered systems. This paper presents a multi-attribute classification fusion approach which leverages the strengths provided by multiple membership classifiers to form a robust classification model for structural health diagnostics. Health diagnosis using the developed approach consists of three primary steps: (i) fusion formulation using a k-fold cross validation model; (ii) diagnostics with multiple multi-attribute classifiers as member algorithms; and (iii) classification fusion through a weighted majority voting with dominance system. State-of-the-art classification techniques from three broad categories (i.e., supervised learning, unsupervised learning, and statistical inference) were employed as the member algorithms. The proposed classification fusion approach is demonstrated with a bearing health diagnostics problem. Case study results indicated that the proposed approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.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.subjectAlgorithmsen_US
dc.subjectBearingsen_US
dc.subjectMaintenanceen_US
dc.subjectSafetyen_US
dc.subjectReliabilityen_US
dc.subjectRobustnessen_US
dc.titleDesign of a robust classification fusion platform for structural health diagnosticsen_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2013 by ASMEen_US


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