Title :
Bayesian framework for aerospace gas turbine engine prognostics
Author :
Zaidan, M.A. ; Mills, A.R. ; Harrison, R.F.
Author_Institution :
Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
Abstract :
Prognostics is an emerging capability of modern health monitoring that aims to increase the fidelity of failure predictions. In the aerospace industry, it is a key technology to maximise aircraft availability, offering a route to increase time in-service and reduce operational disruption through improved asset management.
Keywords :
Bayes methods; aerospace engines; aerospace industry; gas turbines; Bayesian framework; RUL; aerospace gas turbine engine prognostics; aerospace industry; aircraft engine; closed-form solution; failure predictions; health monitoring; operational disruption; physics-based approach; probabilistic estimation; remaining useful life; time in-service; Bayes methods; Degradation; Engines; Indexes; Maintenance engineering; Noise; Turbines;
Conference_Titel :
Aerospace Conference, 2013 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
978-1-4673-1812-9
DOI :
10.1109/AERO.2013.6496856
