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AuthorDaroogheh N.
AuthorMeskin N.
AuthorKhorasani K.
Available date2020-04-01T09:45:58Z
Publication Date2019
Publication NameIEEE Transactions on Control Systems Technology
ResourceScopus
ISSN10636536
URIhttp://dx.doi.org/10.1109/TCST.2018.2870044
URIhttp://hdl.handle.net/10576/13738
AbstractIn this brief, we propose and develop estimation, prediction, and health monitoring methodologies for nonlinear systems by modeling the damage and degradation mechanism dynamics as 'slow' states that are augmented with the system 'fast' states. This augmentation results in a two-time scale (TTS) nonlinear system that is utilized for the development of decoupled slow and fast health estimation and prediction modules within a health monitoring framework. Specifically, a TTS filtering approach based on ensemble Kalman filters is developed by taking advantage of the singular perturbation model reduction technique. Our proposed methodology is then applied to a gas turbine engine that is affected by degradation phenomenon due to the turbine erosion. Extensive comparative studies are conducted to validate and demonstrate the advantages and capabilities of our proposed methodology when compared to the well-known nonlinear particle filtering (PF) approach that is commonly utilized in the literature. - 1993-2012 IEEE.
SponsorManuscript received October 28, 2017; revised April 21, 2018; accepted August 26, 2018. Date of publication September 28, 2018; date of current version October 9, 2019. Manuscript received in final form September 9, 2018. Recommended by Associate Editor H. Fathy. This work was supported by the Natural Sciences and Engineering Research Council of Canada under the Discovery Grants Program. (Corresponding author: Khashayar Khorasani.) N. Daroogheh and K. Khorasani are with the Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada (e-mail: n_daroog@encs.concordia.ca; kash@ece.concordia.ca).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectEnsemble Kalman filtering
gas turbines
nonlinear systems
singularly perturbed systems
state estimation and prediction
TitleEnsemble Kalman Filters for State Estimation and Prediction of Two-Time Scale Nonlinear Systems with Application to Gas Turbine Engines
TypeArticle
Pagination2565-2573
Issue Number6
Volume Number27
dc.accessType Abstract Only


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