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dc.contributor.authorTamilselvan, Prasanna
dc.contributor.authorWang, Pingfeng
dc.date.accessioned2013-03-26T14:49:15Z
dc.date.available2013-03-26T14:49:15Z
dc.date.issued2013-03-13
dc.identifier.citationTamilselvan, Prasanna; Wang, Pingfeng. 2013. Failure Diagnosis Using Deep Belief Learning based Health State Classification. Reliability Engineering & System Safety, Available online 13 March 2013en_US
dc.identifier.issn0951-8320
dc.identifier.urihttp://dx.doi.org/10.1016/j.ress.2013.02.022
dc.identifier.urihttp://hdl.handle.net/10057/5566
dc.descriptionClick on the 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 operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). 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 DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesReliability Engineering & System Safety;Available online 13 March 2013
dc.subjectFault diagnosisen_US
dc.subjectArtificial intelligence in diagnostic classificationen_US
dc.subjectDeep belief networksen_US
dc.titleFailure Diagnosis Using Deep Belief Learning based Health State Classificationen_US
dc.typeArticleen_US


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