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AuthorNavi, M.
AuthorDavoodi, M.R.
AuthorMeskin, Nader
Available date2022-04-14T08:45:42Z
Publication Date2015
Publication NameIFAC-PapersOnLine
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.ifacol.2015.09.719
URIhttp://hdl.handle.net/10576/29800
AbstractIn this paper, partial kernel principal component analysis (PKPCA) is studied for sensor fault detection and isolation of an aeroderivative industrial gas turbine. Principal component analysis (PCA) is an effective tool for process monitoring task, however it can achieve acceptable results only for linear processes. In the case of nonlinear processes such as gas turbines, kernel PCA approach can be used which leads to more accurate health monitoring. In order to achieve fault isolation, partial KPCA is proposed where the parity relation concept is used to generate a set of residual signals. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in an industrial gas turbine can be effectively detected and isolated. 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
SponsorQatar National Research Fund
Languageen
Publisher9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015
SubjectData handling
Gas turbines
Gases
Plant management
Process monitoring
Fault detection
Turbine components
Fault detection and isolation
Health monitoring
Industrial gas turbines
Kernel principal component analyses (KPCA)
Nonlinear process
Residual signals
Sensor fault detection
Simulation studies
Fault detection
Principal component analysis
TitleSensor fault detection and isolation of an industrial gas turbine using partial kernel PCA
TypeConference Paper
Pagination1389-1396
Issue Number21
Volume Number28
dc.accessType Abstract Only


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