基于IMM-UKF方法的机电作动器突发性故障诊断研究
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摘要
飞机飞行控制系统机电作动器(electromechanical actuator,EMA)的突发性故障会影响到飞机的飞行安全性,甚至导致飞机失控.针对EMA的突发性故障,提出一种基于交互式多模型(interactive multiple model,IMM)和无迹卡尔曼滤波(unscented Kalman filter,UKF)相结合的故障诊断方法.该方法利用UKF不仅能更好地逼近状态方程的非线性特性,而且能使滤波器具有更好的稳定性和更低的计算量要求;利用IMM不仅解决了可测量参数偏少导致的故障诊断困难的问题,而且还改善了发生的故障与预先假设的故障差异较大的情况下故障诊断的快速性和准确性.通过对某型EMA进行故障诊断,仿真结果表明所提出的IMM-UKF故障诊断方法可以实现对EMA部件和传感器故障的快速准确诊断.
The abrupt faults of electromechanical actuator(EMA) can cause the safety problem of plane flight control system, so as to the plane lose control. A diagnostic method for abrupt faults of electromechanical actuator(EMA) was proposed based on interactive multiple model(IMM) and unscented Kalman filter(UKF). The UKF was used to make the state equation be close to a nonlinear feature and make the filter represent a better stability and an acceptable computational complexity. And the IMM was used to solve the problem of the diagnosed model with a few measurable parameters, but also to improve the speediness and accuracy of the fault diagnosis in the case of large difference between the occurrence of the fault and the presupposed fault. The effectiveness of proposed IMM-UKF fault diagnosis method of EMA was verified by simulation. The results show that the proposed IMM-UKF method can obtain real-time and accurate diagnostic values of the abrupt faults of components and sensor in EMA.
The abrupt faults of electromechanical actuator(EMA) can cause the safety problem of plane flight control system, so as to the plane lose control. A diagnostic method for abrupt faults of electromechanical actuator(EMA) was proposed based on interactive multiple model(IMM) and unscented Kalman filter(UKF). The UKF was used to make the state equation be close to a nonlinear feature and make the filter represent a better stability and an acceptable computational complexity. And the IMM was used to solve the problem of the diagnosed model with a few measurable parameters, but also to improve the speediness and accuracy of the fault diagnosis in the case of large difference between the occurrence of the fault and the presupposed fault. The effectiveness of proposed IMM-UKF fault diagnosis method of EMA was verified by simulation. The results show that the proposed IMM-UKF method can obtain real-time and accurate diagnostic values of the abrupt faults of components and sensor in EMA.
引文
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[2]Hoyos Velasco C I,Gaeta A.Valveposition-based control at engine key-on of an electro-mechanical valve actuator for camless engines:a robust controller tuning via bifurcation analysis[J].Asian Journal of Control,2016,18(3):868-887.
[3]王小平,蔡远利,于振华,等.一种导弹导引律及参数的非线性MMAE辨识方法[J].飞行力学,2015,33(5):430-434. Wang Xiaoping,Cai Yuanli,Yu Zhenhua,et al.Nonlinear MMAE-based missile guidance law and parameter identification[J].Flight Dynamics,2015,33(5):430-434.(in Chinese)
[4]Lu P,Eykeren L V,Kampen E V,et al.Selective-reinitialization multiple-model adaptive estimation for fault detection and diagnosis[J].Journal of Guidance Control & Dynamics,2015,38(8):1409-1424.
[5]Hwang I,Kim S,Kim Y,et al.A survey of fault detection,isolation,and reconfiguration methods[J].IEEE Transactions on Control Systems Technology,2010,18(3):636-653.
[6]Zhang W,Zhao P,Liu X,et al.EMMAE failure detection system and failure evaluation over flight performance[J].International Journal of Intelligent Computing & Cybernetics,2012,5(5):401-420.
[7]Foo G H B,Zhang X,Vilathgamuwa D M.A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter[J].IEEE Transactions on Industrial Electronics,2013,60(8):3485-3495.
[8]赵辉,周欢,翁兴伟,等.传感器故障条件下的自适应UKF算法[J].控制与决策,2015,30(11):2025-2032. Zhao Hui,Zhou Huan,Weng Xingwei,et al.Adaptive UKF algorithm under condition of sensor faults[J].Control and Decision,2015,30(11):2025-2032.(in Chinese)
[9]Moon C,Kwon Y A.Sensorless speed control of permanent magnet synchronous motor by unscented Kalman filter using various scaling parameters[J].Journal of Electrical Engineering & Technology,2016,11(2):347-352.
[10]Vasuhi S,Vaidehi V.Target tracking using interactive multiple model for wireless sensor network[J].Information Fusion,2016,27(C):41-53.
[11]Keshavarz-Mohammadiyan A,Khaloozadeh H.Interacting multiple model and sensor selection algorithms for maneuvering target tracking in wireless sensor networks with multiplicative noise[J].International Journal of Systems Science,2017,48(5):899-908.