引用本文: 邱岳恒, 赵鹏轩, 陈伟, 王晓光. 飞机机翼损伤的气动模型及故障诊断研究[J]. 力学与实践, 2014, 36(1): 23-28.
QIU Yueheng, ZHAO Pengxuan, CHEN Wei, WANG Xiaoguang. THE FAULT DIAGNOSIS AND AERODYNAMIC MODEL FOR AIRCRAFT WING DAMAGE[J]. MECHANICS IN ENGINEERING, 2014, 36(1): 23-28.
 Citation: QIU Yueheng, ZHAO Pengxuan, CHEN Wei, WANG Xiaoguang. THE FAULT DIAGNOSIS AND AERODYNAMIC MODEL FOR AIRCRAFT WING DAMAGE[J]. MECHANICS IN ENGINEERING, 2014, 36(1): 23-28.

## THE FAULT DIAGNOSIS AND AERODYNAMIC MODEL FOR AIRCRAFT WING DAMAGE

• 摘要: 为了确保机翼损伤后飞机的飞行安全，提出了一种在线的故障诊断方法. 首先，根据输入输出特性，采用遗忘因子递推最小二乘法对飞机故障后的气动导数进行辨识，建立了机翼损伤故障的数学模型；然后，结合多模型方法和中心差分卡尔曼滤波器（central difference Kalman filter，CDKF）各自的优点，实现对机翼损伤的故障诊断，并采用强跟踪滤波器在线更新CDKF 的采样点，以增强CDKF 的自适应能力. 最后，通过仿真结果验证了本文所提方法的有效性.

Abstract: To ensure the flight safety under the condition of the wing damage in the aircraft, a new online fault diagnosis method is proposed based on the nonlinear aerodynamic model of the wing damage. Firstly, according to the input and output characteristics of the aircraft, the aerodynamic derivatives can be identified online using the recursive least squares algorithm with a forgetting factor, and the wing damage model can be established as the aerodynamic derivatives are substituted into the equation of the aircraft motion. Secondly, the fault diagnosis approach is proposed based on the multi-model algorithm and the central difference Kalman filter algorithm, and the adaptive capacity of the central difference Kalman filter algorithm can be strengthened as the sampling points are updated online using the strategy of a strong tracking filter. Lastly, in the presence of various wing damage faults, the simulation results indicate that the proposed algorithm can not only improve the experiment effciency but also ensure the fault coverage as compared with other algorithms.

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