A Sensor Fault Tolerance Strategy Based on Hybrid Redundancy for Aeroengine Control System

Sensors are crucial information sources for aeroengine control system, and the reliability of their measurements directly affects the safety and efficiency of the engine. To enhance the reliability of sensor signals, this paper proposes a sensor fault tolerance strategy based on hybrid redundancy. Specifically, an analytical redundancy model for sensors is constructed using neural networks trained with simulated engine flight data, and a hybrid redundancy fusion algorithm is developed by utilizing the distances between sensor signals. Bias and drift faults are injected into the sensors, and simulations are conducted to verify the effectiveness of the proposed method. The verification results show that the proposed method can provide analytical redundancy for typical gas path sensors and enhance the fault tolerance ability of the control system.