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dc.contributor.authorWang, Pingfeng
dc.contributor.authorYoun, Byeng D.
dc.contributor.authorHu, Chao
dc.date.accessioned2012-04-23T17:00:16Z
dc.date.available2012-04-23T17:00:16Z
dc.date.issued2012-04
dc.identifier.citationWang, P., B.D. Youn, and C. Hu. 2012. "A generic probabilistic framework for structural health prognostics and uncertainty management". Mechanical Systems and Signal Processing. 28: 622-637.en_US
dc.identifier.issn0888-3270
dc.identifier.otherWOS: 000301549900045
dc.identifier.urihttp://hdl.handle.net/10057/5083
dc.identifier.urihttp://dx.doi.org/10.1016/j.ymssp.2011.10.019
dc.descriptionClick on the DOI link below to access the article (may not be free).en_US
dc.description.abstractStructural health prognostics can be broadly applied to various engineered artifacts in an engineered system. However, techniques and methodologies for health prognostics become application-specific. This study thus aims at formulating a generic framework of structural health prognostics, which is composed of four core elements: (i) a generic health index system with synthesized health index (SHI), (ii) a generic offline learning scheme using the sparse Bayes learning (SBL) technique, (iii) a generic online prediction scheme using the similarity-based interpolation (SBI), and (iv) an uncertainty propagation map for the prognostic uncertainty management. The SHI enables the use of heterogeneous sensory signals; the sparseness feature employing only a few neighboring kernel functions enables the real-time prediction of remaining useful lives (RULs) regardless of data size; the SBI predicts the RULs with the background health knowledge obtained under uncertain manufacturing and operation conditions; and the uncertainty propagation map enables the predicted RULs to be loaded with their statistical characteristics. The proposed generic framework of structural health prognostics is thus applicable to different engineered systems and its effectiveness is demonstrated with two cases studies.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesMechanical Systems and Signal Processing;2012,v.28
dc.subjectHealth prognosticsen_US
dc.subjectSparse bayes learningen_US
dc.subjectRemaining useful lifeen_US
dc.subjectSimilarityen_US
dc.subjectSynthesized health indexen_US
dc.subjectUncertainty managementen_US
dc.subject.classificationENGINEERING
dc.titleA generic probabilistic framework for structural health prognostics and uncertainty managementen_US
dc.typeArticleen_US
dc.description.versionPeer reviewed
dc.rights.holderCopyright © 2012, Elsevier


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