  In this paper, a novel sensor fault detection, isolation and identification
(FDII) strategy is proposed by using the multiple model (MM) approach. The
scheme is based on multiple hybrid Kalman filters (HKF) 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 multiple HKF-based FDI scheme is
extended to identify the magnitude of a sensor fault by using a modified
generalized likelihood ratio (GLR) method which 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 on-line
hierarchical multiple HKF-based FDII scheme under different flight modes.
Finally, our proposed HKF-based FDI approach is compared with various filtering
methods such as the linear, extended, unscented and cubature Kalman filters
(LKF, EKF, UKF and CKF, respectively) corresponding to both interacting and
non-interacting multiple model (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 parameters degradations.
