Show simple item record

dc.contributor.authorHu, Chao
dc.contributor.authorYoun, Byeng D.
dc.contributor.authorWang, Pingfeng
dc.contributor.authorYoon, Joung Taek
dc.date.accessioned2012-03-22T15:40:21Z
dc.date.available2012-03-22T15:40:21Z
dc.date.issued2012-03
dc.identifier.citationHu, Chao; Youn, Byeng D.; Wang, Pingfeng and Joung Taek, Yoon. 2012. Ensemble of data-driven prognostic algorithms for robust prediction of Remaining Useful Life. Reliability Engineering & System Safety, Available online 17 March 2012en_US
dc.identifier.issn0951-8320
dc.identifier.urihttp://hdl.handle.net/10057/4920
dc.identifier.urihttp://dx.doi.org/10.1016/j.ress.2012.03.008
dc.descriptionClick on the DOI link below to access the article (may not be free).en_US
dc.description.abstractPrognostics aims at determining whether a failure of an engineered system (e.g., a nuclear power plant) is impending and estimating the remaining useful life (RUL) before the failure occurs. The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust; (ii) it wastes the resources for constructing the algorithms that are discarded; (iii) it requires the testing data in addition to the training data. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesReliability Engineering & System Safety;2012
dc.subjectEnsembleen_US
dc.subjectK-fold cross validationen_US
dc.subjectWeighting schemesen_US
dc.subjectData-driven prognosticsen_US
dc.subjectRUL predictionen_US
dc.titleEnsemble of data-driven prognostic algorithms for robust prediction of Remaining Useful Lifeen_US
dc.typeArticleen_US
dc.description.versionPeer reviewed
dc.rights.holderCopyright © 2012, Elsevier


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record