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dc.contributor.authorWang, Pingfeng
dc.contributor.authorTamilselvan, Prasanna
dc.contributor.authorHu, Chao
dc.identifier.citationWang, Pingfeng; Tamilselvan, Prasanna; Hu, Chao. 2014. Health diagnostics using multi-attribute classification fusion. Engineering Applications of Artificial Intelligence, vol. 32, June 2014:ppg. 192–202en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThis paper presents a classification fusion approach for health diagnostics that can leverage the strengths of multiple member classifiers to form a robust classification model. 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 approach. State-of-the-art classification techniques from three broad categories (i.e., supervised learning, unsupervised learning, and statistical inference) were employed as member algorithms. The diagnostics results from the fusion approach will be better than, or at least as good as, the best result provided by all individual member algorithms. The developed classification fusion approach is demonstrated with the 2008 PHM challenge problem and rolling bearing health diagnostics problem. Case study results indicated that, in both problems, the developed fusion diagnostics approach outperforms any stand-alone member algorithrn with better diagnostic accuracy and robustness. (C) 2014 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipNational Science Foundation (CMMI-1200597), and Spirit AeroSystems Inc (PO-4400221590).en_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofseriesEngineering Applications of Artificial Intelligence;v.32
dc.subjectFault diagnosisen_US
dc.subjectMachine learningen_US
dc.subjectClassification fusionen_US
dc.titleHealth diagnostics using multi-attribute classification fusionen_US

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