| Citation: | WANG Jian, WANG Xinmin, XIE Rong, et al. Gradual fault diagnosis for electromechanical actuator based on DWNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1831-1837. doi: 10.13700/j.bh.1001-5965.2018.0769(in Chinese)
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It is difficult to accurately predict the gradual failure of electromechanical actuator (EMA) in an aircraft flight control system. The flight safety of an aircraft will be affected by these faults if they are not detected in early stage. A fault diagnosis method based on dynamic wavelet neural network (DWNN) is proposed to diagnose the gradual fault of EMA. This method trains the DWNN fault diagnosis model in offline step by using EMA's operation data of gradual faults, such as interturn short circuit of armature winding, screw and ball wear of transmission device, and then the trained DWNN model is used to diagnose the gradual faults of EMA online. The innovations of the research are as follows:First, the influence of the high-frequency components in the sensor measurement signals is removed using wavelet decomposition algorithm in the DWNN model; Second, the information input in the past and the information predicted in the past are integrated using the memory ability of the feedback neural network, so the accuracy of EMA gradual fault diagnosis is improved. The simulation results obtained from tests on a specific EMA show that the proposed DWNN method can accurately diagnose the gradual fault of EMA components.
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