Dual-redundancy sensor fault diagnosis of fuel metering unit based on real-time residual window

In this paper, an effective approach is developed to diagnose sensor faults of fuel metering unit (FMU) control system. The accurate mathematical model of FMU is built and features of sensor fault are analyzed. To improve the accuracy of sensor diagnosis, Kalman filters are utilized to estimate the optimal outputs of dual-redundancy sensors both in the presence of process noise and measurement noise. However, the diagnosis results depend on the accuracy of system model, especially In the dynamic process. Therefore, an Improved residual checking method based on a real time moving detection window is proposed to reduce the false alarm rate In addition, the design criteria of window width and threshold value are given according to the requirements of control system. The proposed method is verified via simulation. Results show that when faults occur in a sensor, they can be accurately detected in time. Moreover, the method effectively avoids false alarm during the rising stage when the operating point changes.