A new hybrid prognostic methodology

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dc.contributor.author Eker, Omer F.
dc.contributor.author Camci, Fatih
dc.contributor.author Jennions, Ian K.
dc.date.accessioned 2020-02-07T11:43:06Z
dc.date.available 2020-02-07T11:43:06Z
dc.date.issued 2019-03-08
dc.identifier.citation Eker O, Camci F, Jennions IK. (2019) A new hybrid prognostic methodology. International Journal of Prognostics and Health Management, Volume 10, March 2019, Article number 009 en_UK
dc.identifier.issn 2153-2648
dc.identifier.uri http://www.phmsociety.org/node/2559
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/15110
dc.description.abstract Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available. en_UK
dc.language.iso en en_UK
dc.publisher Prognostics and Health Management Society en_UK
dc.rights Attribution 3.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/ *
dc.subject hybrid algorithms en_UK
dc.subject similarity-based modelling en_UK
dc.subject physical modelling en_UK
dc.subject empirical model en_UK
dc.title A new hybrid prognostic methodology en_UK
dc.type Article en_UK

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