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dc.contributor.authorTamilselvan, Prasanna
dc.contributor.authorWang, Yibin
dc.contributor.authorWang, Pingfeng
dc.date.accessioned2012-11-29T20:32:53Z
dc.date.available2012-11-29T20:32:53Z
dc.date.issued2012
dc.identifier.citationTamilselvan, Prasanna; Wang, Yibin; Wang, Pingfeng. 2012. Deep belief network based state classification for structural health diagnosis. 2012 IEEE Aerospace Conferenceen_US
dc.identifier.issn1095-323X
dc.identifier.otherWOS:000309105303086
dc.identifier.urihttp://hdl.handle.net/10057/5397
dc.identifier.urihttp://dx.doi.org/10.1109/AERO.2012.6187366
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractEffective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Networks (DBN). The DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked Restricted Boltzmann Machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using the DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing the sensory data for DBN training and testing; second, developing DBN based classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. The performance of health diagnosis using DBN based health state classification is compared with support vector machine technique and demonstrated with aircraft wing structure health diagnostics and aircraft engine health diagnosis using 2008 PHM challenge data.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2012 IEEE Aerospace Conference;
dc.subjectFault diagnosisen_US
dc.subjectartificial intelligence in diagnostic classificationen_US
dc.subjectdeep belief networksen_US
dc.titleDeep belief network based state classification for structural health diagnosisen_US
dc.typeConference paper


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