A generic probabilistic framework for structural health prognostics and uncertainty management

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dc.contributor.author Wang, Pingfeng
dc.contributor.author Youn, Byeng D.
dc.contributor.author Hu, Chao
dc.date.accessioned 2012-04-23T17:00:16Z
dc.date.available 2012-04-23T17:00:16Z
dc.date.issued 2012-04
dc.identifier.citation Wang, 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.issn 0888-3270
dc.identifier.other WOS: 000301549900045
dc.identifier.uri http://hdl.handle.net/10057/5083
dc.identifier.uri http://dx.doi.org/10.1016/j.ymssp.2011.10.019
dc.description Click on the DOI link below to access the article (may not be free). en_US
dc.description.abstract Structural 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.iso en_US en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Mechanical Systems and Signal Processing;2012,v.28
dc.subject Health prognostics en_US
dc.subject Sparse bayes learning en_US
dc.subject Remaining useful life en_US
dc.subject Similarity en_US
dc.subject Synthesized health index en_US
dc.subject Uncertainty management en_US
dc.subject.classification ENGINEERING
dc.title A generic probabilistic framework for structural health prognostics and uncertainty management en_US
dc.type Article en_US
dc.description.version Peer reviewed
dc.rights.holder Copyright © 2012, Elsevier

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