Accommodating repair actions into gas turbine prognostics

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dc.contributor.authorSkaf, Zakwan
dc.contributor.authorZaidan, Martha A.
dc.contributor.authorHarrison, Robert F.
dc.contributor.authorMills, Andrew R.
dc.date.accessioned2016-07-29T12:16:41Z
dc.date.available2016-07-29T12:16:41Z
dc.date.issued2013-10-08
dc.description.abstractElements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations, leading to significant uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy. In this paper, we adopt Bayesian on-line change point detection to detect the compressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life. To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compressor washing events for a gas turbine and thus provide demonstrably improved prognosis.en_UK
dc.identifier.citationZakwan Skaf, Martha A Zaidan, Robert F Harrison, and Andrew R Mills. Accommodating repair actions into gas turbine prognostics. Proceedings of the Annual Conference of the Prognostics and Health Management Society, 14-17 October 2013, New Orleans, USA.en_UK
dc.identifier.isbn978-1-936263-06-6
dc.identifier.issn2325-0178
dc.identifier.urihttp://ftp.phmsociety.org/sites/phmsociety.org/files/phm_submission/2013/phmc_13_014.pdf
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10192
dc.language.isoenen_UK
dc.publisherPHM Societyen_UK
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.subjectData-driven prognosticsen_UK
dc.subjectchange detectionen_UK
dc.subjectBayesian inferenceen_UK
dc.titleAccommodating repair actions into gas turbine prognosticsen_UK
dc.typeArticleen_UK

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