A Statistical Method for the Detection of Sensor Abrupt Faults in Aircraft Control Systems

Aircraft sensors are important for proper operation and safety, and their condition is conventionally monitored based upon the hardware redundancy principle. In this work a statistical method capable of independently monitoring a single sensor, and thus enhancing reliability and overall system safety, is introduced. The method's main advantages are simplicity, applicability to a wide variety of aircraft operating conditions, the handling of uncertainties, no need for additionally monitored signals, and no need for physics based aircraft dynamics models. The method is based on a statistical time series framework accounting for random effects and uncertainties, and exploits the fact that abrupt faults are characterized by time constants smaller than those of the aircraft. It employs monitored signal nonstationarity removal, signal whitening via novel pooled autoregressive modeling, statistical decision making, as well as electronic spike/glitch removal logic. The method effectiveness is demonstrated within the simulation environment of a small commercial aircraft via test cases and Monte Carlo experiments with abrupt faults occurring in an angle-of-attack sensor.