Accommodating repair actions into gas turbine prognostics

Show simple item record Skaf, Zakwan Zaidan, Martha A. Harrison, Robert F. Mills, Andrew R. 2016-07-29T12:16:41Z 2016-07-29T12:16:41Z 2013-10-08
dc.identifier.citation Zakwan 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.isbn 978-1-936263-06-6
dc.identifier.issn 2325-0178
dc.description.abstract Elements 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.language.iso en en_UK
dc.publisher PHM Society en_UK
dc.rights This 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.subject Data-driven prognostics en_UK
dc.subject change detection en_UK
dc.subject Bayesian inference en_UK
dc.title Accommodating repair actions into gas turbine prognostics en_UK
dc.type Article en_UK

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