In this paper, a novel sensor fault detection, isolation and identification(FDII) strategy is proposed by using the multiple model (MM) approach. Thescheme is based on multiple hybrid Kalman filters (HKF) which represents anintegration of a nonlinear mathematical model of the system with a number ofpiecewise linear (PWL) models. The proposed fault detection and isolation (FDI)scheme is capable of detecting and isolating sensor faults during the entireoperational regime of the system by interpolating the PWL models using aBayesian approach. Moreover, the proposed multiple HKF-based FDI scheme isextended to identify the magnitude of a sensor fault by using a modifiedgeneralized likelihood ratio (GLR) method which relies on the healthyoperational mode of the system. To illustrate the capabilities of our proposedFDII methodology, extensive simulation studies are conducted for a nonlineargas turbine engine. Various single and concurrent sensor fault scenarios areconsidered to demonstrate the effectiveness of our proposed on-linehierarchical multiple HKF-based FDII scheme under different flight modes.Finally, our proposed HKF-based FDI approach is compared with various filteringmethods such as the linear, extended, unscented and cubature Kalman filters(LKF, EKF, UKF and CKF, respectively) corresponding to both interacting andnon-interacting multiple model (MM) based schemes. Our comparative studiesconfirm the superiority of our proposed HKF method in terms of promptness ofthe fault detection, lower false alarm rates, as well as robustness withrespect to the engine health parameters degradations.
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