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AuthorTsoutsanis, E.
AuthorMeskin, Nader
Available date2022-04-14T08:45:41Z
Publication Date2017
Publication NameEnergy
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.energy.2017.04.006
URIhttp://hdl.handle.net/10576/29792
AbstractThe domination of gas turbines in the energy arena is facing many challenges from environmental regulations and the plethora of renewable energy sources. The gas turbine has to operate under demand-driven modes and its components consume their useful life faster than the engines of the base-load operation era. As a result the diagnostics and prognostics tools should be further developed to cope with the above operation modes and improve the condition based maintenance (CBM). In this study, we present a derivative-driven diagnostic pattern analysis method for estimating the performance of gas turbines under dynamic conditions. A real time model-based tuner is implemented through a dynamic engine model built in Matlab/Simulink for diagnostics. The nonlinear diagnostic pattern is then partitioned into data-windows. These are the outcome of a data analysis based on the second order derivative which corresponds to the acceleration of degradation. Linear regression is implemented to locally fit the detected deviations and predict the engine behavior. The accuracy of the proposed method is assessed through comparison between the predicted and actual degradation by the remaining useful life (RUL) metric. The results demonstrate and illustrate an improved accuracy of our proposed methodology for prognostics of gas turbines under dynamic modes. 2017 Elsevier Ltd
SponsorQatar Foundation; Qatar National Research Fund
Languageen
PublisherElsevier Ltd
SubjectData handling
Environmental regulations
Gases
Regression analysis
Renewable energy resources
Condition based maintenance
Diagnostics and prognostics
Gas turbine performance
Nonlinear diagnostics
Remaining useful lives
Renewable energy source
Second order derivatives
Window-based
Gas turbines
accuracy assessment
energy resource
engine
maintenance
methodology
performance assessment
real time
regression analysis
software
turbine
TitleDerivative-driven window-based regression method for gas turbine performance prognostics
TypeArticle
Pagination302-311
Volume Number128
dc.accessType Abstract Only


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