An integrated nonlinear model-based approach to gas turbine engine sensor fault diagnostics

Abstract

Aircraft engine sensor fault diagnosis is closely related technology that assists operators in managing the health of gas turbine engine assets. As all gas turbine engines will exhibit performance changes due to usage, the on-board engine model built up initially will no longer track the engine over the course of the engine’s life, and then the model-based method for sensor fault diagnosis tends to be failure. This necessitates the study of the sensor fault diagnosis techniques due to usage over its operating life. Based on our recent results, an integrated approach based on nonlinear on-board model is developed for the gas turbine engine sensor fault diagnostics in this paper. The architecture is mainly composed of dual nonlinear engine models; one is a nonlinear real-time adaptive performance model and the other a nonlinear on-board baseline model. The extended Kalman filter estimator in the nonlinear real-time adaptive performance model is used to obtain the real-time estimates of component performance, and the nonlinear on-board baseline model with performance periodically update to provide the nominal reference in flight. The novel update strategy to sensor fault threshold based on the model errors and noise level is also presented. Important results are obtained on step fault and pulse fault behavior of the engine sensor. The proposed approach is easy to design and tune with long-term engine health degradation. Finally, experiment studies are provided to validate the benefit of the engine sensor fault diagnostics.