基于多模型简化CDKF的故障诊断方法

邱岳恒; 章卫国; 赵鹏轩; 刘小雄

基于多模型简化CDKF的故障诊断方法

1.
西北工业大学自动化学院, 西安 710072
2. 中航工业西安飞机设计研究所飞控液压所, 西安 710089

基金项目: 航空科学基金资助项目(20100753009)

详细信息

中图分类号: TP 273
计量
- 文章访问数: 1644
- HTML全文浏览量: 256
- PDF下载量: 462
- 被引次数: 0
出版历程
- 收稿日期: 2012-07-30
- 网络出版日期: 2013-07-30

Fault diagnosis approach based on multiple model estimator with simplified CDKF

1.
College of Automation, Northwestern Polytechnical University, Xi’an 710072, China
2. Flight Control and Hydraulic Institute, Xi’an Aircraft Design Institute of AVIC, Xi’an 710089, China

摘要

摘要: 针对使用传统卡尔曼滤波器对非线性系统进行故障诊断,估计精度低的问题,提出了一种新的故障诊断方法.该方法结合多模型自适应估计和简化中央差分卡尔曼滤波器的优点,能在线快速地检测出故障,利用中央差分代替了雅可比矩阵的求解,使系统状态估计准确收敛到真实值附近,而且避免了反复求解量测预测方差等一系列繁杂过程.在执行机构不同故障的情况下,通过与其他算法进行诊断对比,结果表明提出的算法在精度上和运行速度上具有明显的优势.
- 非线性系统 /
- 故障诊断 /
- 多模型自适应估计 /
- 简化中央差分卡尔曼滤波器 /
- 执行机构
Abstract: As the direct application of traditional Kalman filter to fault diagnosis of the nonlinear system usually results to low estimation accuracy, a new fault diagnosis approach was proposed. The method combines the multitude model adaptive estimation and the simplified central difference Kalman filter. Therefore, it achieves on-line fault detection rapidly, and makes the state estimation values converge to real values correctly benefits from the replacement of the Jacobian matrix calculation by central difference transformation. Moreover, the repeated process of solving measurement equations and variances is avoided. In the presents of various actuator faults, the simulation results indicate the effectiveness and rapidity of the proposed algorithm compared with the other filters.
- nonlinear system /
- fault diagnosis /
- multitude model adaptive estimation /
- simplified central difference Kalman filter /
- actuator

HTML全文

参考文献(11)

[1]	Liu Z J,Li Q,Liu X H,et al.An expected-mode augmentation based approach for multiple-fault detection and diagnosis in flight control systems[J].Proceedings of the Institution of Mechanical Engineers,Part G,Journal of Aerospace Engineering,2012,226(10):1202-1213
[2]	Ducard G,Geering H P.A reconfigurable flight control system based on the EMMAE method //Proceedings of the 2006 American Control Conference.Piscatway,NJ:IEEE,2006:14-16
[3]	Rupp D,Ducard G,Shafai E,et al.Extended multiple model adaptive estimation for the detection of sensor and actuator faults //44th IEEE Conference on Decision and Control.Piscataway,NJ:IEEE Computer Society,2005:3079-3084
[4]	Qiu Yueheng,Zhang Weiguo.EMMAE failure detection system and failure evaluation over flight performance[J].International Journal of Intelligent Computing and Cybernetics,2012,3(5):401-420
[5]	杨晓华,邵宗战,侯宝娥,等.基于采样的非线性滤波方法[J].指挥控制与仿真,2009,31(5):1-5 Yang Xiaohua,Shao Zongzhan,Hou Baoe,et al.Nonlinear filtering methods based on sampling[J].Command Control & Simulation,2009,31(5):1-5(in Chinese)
[6]	van der Merwe R.Sigma-point Kalman filters for probabilistic inference in dynamic state-space models .Eugune,USA:Oregon Health and Science University,2004
[7]	Kim C,Sakthivel R,Chung W K.Unscented fast SLAM:a robust and efficient solution to the SLAM problem[J].IEEE Transactions on Robotics,2008,24(4):808-820
[8]	Kazufumi Ito,Xiong Kaiqi.Gaussian filters for nonlinear filtering problems [J].IEEE Transactions on Automatic Control,2000,45(5):910- 927
[9]	Norgaard M,Poulsen N K,Ravn O.Advances in derivative free state estimation for nonlinear systems .IMM-Technical Report-1998-15,1998
[10]	严恭敏,严卫生,徐德民.简化UKF滤波在SINS大失准角初始对准中的应用[J].中国惯性技术学报,2008,20(3):253-264 Yan Gongmin,Yan Weisheng,Xu Demin.Application of simplified UKF in SINS initial alignment for large misalignment angles[J].Journal of Chinese Inertial Technology,2008,20(3):253-264(in Chinese)
[11]	鲁道夫·布罗克豪斯.飞行控制[M].北京:国防工业出版社,1999 Rudolf Brockhaus.Flight control[M].Beijing:National Defense Industry Press,1999