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This paper presents an actuator Fault Detection and Isolation (FDI) scheme for nonlinear systems. A state space approach is used and a nonlinear-in-parameters neural network (NLPNN) is employed to identify the additive unknown fault. The FDI scheme is based on a hybrid model (composed of an analytical nominal model and a neural network model) of the nonlinear system. The nominal performance of the system in fault free operation is governed by analytical model whereas the uncertainties and unmodeled dynamics are accounted for by intelligent (neural network based) model. The neural network weights are updated based on a modified backpropagation scheme. The stability of the overall fault detection scheme is shown using Lyapunov's direct method. To evaluate the performance of the proposed fault detection scheme, FDI in a spacecraft attitude control systems with reaction wheel type of actuators is considered as a case study. Simulation results are presented to show the effectiveness of the proposed fault detection scheme.