Kalman Filtering with Inequality Constraints for Certain Turbofan Engine Sensors Fault Diagnosis

Kalman filters are often used to estimate the state variables of a dynamic system. This paper proposed a method of Kalman filtering with linear inequality constraints and applied to certain turbofan aeroengine sensors fault diagnosis. It not only maintains the state variable estimates within a user-defined limit, but also ensures that the state variable estimates vary smoothly and slowly with time and eliminates noise effectively. At the same time, system unmodeled dynamics and random noise disturbance are taken into account in the turbofan engine model. The linearized filtering results demonstrate Kalman filter can estimate state parameters with an average error of less than 0.3%, and the Kalman filtering with linear inequality constraints performs better than the unconstrained filtering. So the proposed method is highly reliable and highly accurate in fault detection and isolation