Show simple item record

AuthorPourbabaee, Bahareh
AuthorMeskin, Nader
AuthorKhorasani, Khashayar
Available date2021-09-01T10:02:48Z
Publication Date2016
Publication NameIEEE Transactions on Control Systems Technology
ResourceScopus
URIhttp://dx.doi.org/10.1109/TCST.2015.2480003
URIhttp://hdl.handle.net/10576/22414
AbstractIn this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations. 2015 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBayesian networks
Engines
Fault tolerant computer systems
Gas turbines
Kalman filters
Piecewise linear techniques
Cubature kalman filters
Extensive simulations
Fault detection and isolation schemes
Generalized likelihood ratio method
Nonlinear gas turbines
Nonlinear mathematical model
Piecewise linear models
Sensor fault detection
Fault detection
TitleSensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines
TypeArticle
Pagination1184-1200
Issue Number4
Volume Number24
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record