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dc.contributor.authorBai, Guangxing
dc.contributor.authorWang, Pingfeng
dc.date.accessioned2016-09-29T18:35:21Z
dc.date.available2016-09-29T18:35:21Z
dc.date.issued2016-09
dc.identifier.citationG. Bai and P. Wang, "Prognostics Using an Adaptive Self-Cognizant Dynamic System Approach," in IEEE Transactions on Reliability, vol. 65, no. 3, pp. 1427-1437, Sept. 2016en_US
dc.identifier.issn0018-9529
dc.identifier.otherWOS:000382714400025
dc.identifier.urihttp://dx.doi.org/10.1109/TR.2016.2570542
dc.identifier.urihttp://hdl.handle.net/10057/12449
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractPrognostics and health management is an emerging engineering technology that has been applied to a large variety of engineering systems to improve system's reliability. However, existing prognostics approaches have been developed largely based upon specific applications and system models, thus possess limited general applicability. This paper presents a generic data-driven prognostics method, namely an adaptive self-cognizant dynamic system (ASDS) approach, that integrates adaptive system recognition with a general state space-based dynamic system model for remaining useful life (RUL) prediction. The developed approach formulates a statistical learning framework with three core attributes: 1) a state-space-based dynamic system approach for the system performance modeling in general, 2) a data-driven method to learn time-series degradation performance of an engineering system, and 3) a Bayesian technique for self-updating of data-driven models to adapt to the operational or environmental changes. With the developed ASDS approach, the prognostics technique can eliminate the dependence on system specific models and be adaptive to system performance changes due to degradation or variation of system operational conditions, thereby yielding accurate RUL predictions. The developed methodology is applied to two engineering case studies to demonstrate its effectiveness.en_US
dc.description.sponsorshipNational Science Foundation under the faculty CAREER Award CMMI-1351414, the Award CMMI-1538508, and the Department of Transportation through University Transportation Center (UTC) Program.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Transactions on Reliability;v.65:no.3
dc.subjectData-drivenen_US
dc.subjectHealth managementen_US
dc.subjectNonlinear autoregressive modelen_US
dc.subjectParticle filter (PF)en_US
dc.subjectPrognosticsen_US
dc.subjectReliabilityen_US
dc.subjectRemaining useful life (RUL)en_US
dc.titlePrognostics using an adaptive self-cognizant dynamic system approachen_US
dc.typeArticleen_US
dc.rights.holder© Copyright 2016 IEEE - All rights reserved.en_US


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