Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

Abstract:

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

• Periodical: Applied Mechanics and Materials (Volumes 764-765)
• Pages: 740-746
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.764-765.740
• Online since: May 2015
• Authors: Hang Yuan, Chen Lu, Ze Tao Xiong, Hong Mei Liu*
• Keywords: Aileron Actuator, Fault Detection, Neural Network (NN), Variable Conditions

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