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AuthorPourbabaee, Bahareh
AuthorMeskin, Nader
AuthorKhorasani, Khashayar
Available date2021-09-01T10:02:39Z
Publication Date2016
Publication NameMechanical Systems and Signal Processing
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.ymssp.2016.02.023
URIhttp://hdl.handle.net/10576/22337
AbstractIn this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature. 2016 Elsevier Ltd. All rights reserved.
Languageen
PublisherAcademic Press
SubjectAircraft propulsion
Bayesian networks
Engines
Gas turbines
Kalman filters
Linear matrix inequalities
Matrix algebra
Parameter estimation
Piecewise linear techniques
Riccati equations
Spurious signal noise
Synthetic aperture sonar
Time varying control systems
Uncertainty analysis
Bayesian approaches
Fault detection and isolation
Multiple-modeling
Piecewise linear modeling
Robust Kalman filters
Fault detection
TitleRobust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties
TypeArticle
Pagination136-156
Volume Number76-77
dc.accessType Abstract Only


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