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dc.contributor.authorTamilselvan, Prasanna
dc.contributor.authorWang, Pingfeng
dc.identifier.citationTamilselvan, Prasanna; Wang, Pingfeng. 2015. A tri-fold hybrid classification approach for diagnostics with unexampled faulty states. Mechanical Systems and Signal Processing, vol. 50–51, January 2015:pp 437–455en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractSystem health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing system complexity, it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled system faulty states based upon sensory data to avoid sudden catastrophic system failures. This paper presents a trifold hybrid classification (THC) approach for structural health diagnosis with unexampled health states (UHS), which comprises of preliminary VHS identification using a new thresholded Mahalanobis distance (TMD) classifier, VHS diagnostics using a two-class support vector machine (SVM) classifier, and exampled health states diagnostics using a multi-class SVM classifier. The proposed THC approach, which takes the advantages of both TMD and SVM-based classification techniques, is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the exampled health states and forming new ones autonomously. The proposed THC approach is further extended to a generic framework for health diagnostics problems with unexampled faulty states and demonstrated with health diagnostics case studies for power transformers and rolling bearings. (C) 2014 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipNational Science Foundation (CMMI-1200597) and Wichita State University through the University Research Creative Project Awards (UCRA).en_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofseriesMechanical Systems and Signal Processing;v.50-51
dc.subjectFault diagnosticsen_US
dc.subjectHealth monitoringen_US
dc.subjectUnexampled faultyen_US
dc.titleA tri-fold hybrid classification approach for diagnostics with unexampled faulty statesen_US
dc.rights.holderCopyright © 2014 Elsevier Ltd. All rights reserved.

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