A single fault diagnosis and location method of aero-engine actuator and its sensor was proposed. Fault of actuator was diagnosed according to the actuator model and located according to the inverse engine model. The actuator model was built based on offline training BP(back propagation) neural network (NN). The network parameters were trained according to the test data of a semi-physical simulation test bed. The inverse engine model based on offline and online extreme learning machine (ELM) had original diagnosis ability and adaptability during operation. The training samples were selected using the threshold discrimination

which can reduce the online training time and improve the real-time property of inverse model during the online training process. Design and simulation results of a certain engine fuel system actuator show that

the proposed system can diagnose and locate the single faults of engine actuator or its sensor accurately in steady state

dynamic state and degradation state. The real-time property can be satisfied.