Neural network-based fault detection method for aileron actuator

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. A fault detection method for aileron actuator under variable conditions is proposed in this study. In the approach, three neural networks are used for fault detection and preliminary fault localization. The first neural network, which is employed as an observer, is established to monitor the aileron actuator and estimate the system output. The second neural network generates the corresponding adaptive threshold synchronously. The last neural network is used as a force motor current observer, and outputs estimated force motor current. Faults are detected by comparing the residual error (the difference value between the actual and estimated output) and the threshold, or comparing the force motor current and the estimated force motor current. In considering of the variable conditions, aerodynamic loads are introduced to the neural network, and the training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.