Performance assessment for aileron actuators based on MF-DFA and SOM neural network

To improve the robustness of performance assessment for aileron actuators, a novel performance assessment for aileron actuators using multi-fractal detrended fluctuation analysis (MF-DFA) and self-organizing mapping (SOM) neural network is first proposed. First, a generalized regression neural network (GRNN) is employed to establish a fault observer, then the residue is generated by subtracting the actual actuator system output from the observer output. Second, the residue's feature is extracted by MF-DFA. Third, SOM neural network, trained only by the normal residue's feature, is used to obtain the minimum quantization error (MQE) between the current feature and the normal feature, and the health confidence value (CV) is obtained by normalizing MQE. Lastly, four types of faults, namely electronic amplifier gain abrupt change, electronic amplifier gain gradual change, sensor gain change and cylinder leakage, are applied to validate the effectiveness of the proposed method.