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基于SMO-SVR的飞机舵面损伤故障趋势预测

董磊 任章 李清东

董磊, 任章, 李清东等 . 基于SMO-SVR的飞机舵面损伤故障趋势预测[J]. 北京航空航天大学学报, 2012, 38(10): 1300-1305.
引用本文: 董磊, 任章, 李清东等 . 基于SMO-SVR的飞机舵面损伤故障趋势预测[J]. 北京航空航天大学学报, 2012, 38(10): 1300-1305.
Dong Lei, Ren Zhang, Li Qingdonget al. Fault prediction for aircraft control surface damage based on SMO-SVR[J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, 38(10): 1300-1305. (in Chinese)
Citation: Dong Lei, Ren Zhang, Li Qingdonget al. Fault prediction for aircraft control surface damage based on SMO-SVR[J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, 38(10): 1300-1305. (in Chinese)

基于SMO-SVR的飞机舵面损伤故障趋势预测

基金项目: 国家自然科学基金资助项目(60874117, 61101004)
详细信息
  • 中图分类号: TP 206+.3

Fault prediction for aircraft control surface damage based on SMO-SVR

  • 摘要: 飞机舵面出现损伤时,为了更准确的预测状态参量变化情况,提出了一种改进的序贯最小优化支持向量回归(SMO-SVR, Sequential Minimal Optimization Support Vector Regression)预测方法.采用改进C-C平均方法对多元时间序列进行相空间重构,以确定最优嵌入维数m和延迟时间τd.根据所求mτd建立加权SVR预测模型,并调整了SMO算法的停机准则.利用区间自适应粒子群算法(IAPSO, Interval Adaptive Particle Swarm Optimization)优化SVR参数,以提高参数优化速度.为了验证改进算法的有效性,针对飞机方向舵损伤故障趋势进行了预测和分析,并与径向基函数神经网络(RBFNN, Radial Basis Function Neural Network)方法进行了对比,仿真结果表明SMO-SVR预测模型具有很好的预测能力.

     

  • [1] Belkharraz A I,Sobel K.Direct adaptive control for aircraft control surface failures //Proceedings of the American Control Conference.Piscataway,NJ,USA:IEEE,2003:3905-3910
    [2] Weiss J L,Willsky A S,Looze D P,et al.Detection and isolation of control surface effectiveness failures in high performance aircraft //Proceedings of the National Aerospace and Electronics Conference.Burlington,MA,US:ALPHATECH,Inc,1985:552-559
    [3] 张怡哲,邓建华.舵面损伤在线故障模式预测及故障检测[J].西北工业大学学报,2003,21(3):298-301 Zhang Yizhe,Deng Jianhua.On-line fault mode prediction and fault detection for control surface damage [J].Journal of Northwestern Polytechnical University,2003,21(3):298-301 (in Chinese)
    [4] 李斌,章卫国,宁东方,等.基于神经网络技术的飞机舵面故障趋势预测研究[J].系统仿真学报,2008,20(21):5840-5842 Li Bin,Zhang Weiguo,Ning Dongfang,et al.Fault prediction system of airplane steer surface based on neural network model [J].Journal of System Simulation,2008,20(21):5840-5842 (in Chinese)
    [5] Kim H S,Eykholt R,Salas J D.Nonlinear dynamics,delay times,and embedding windows [J].Physica:D,1999,127:48-60
    [6] Kugiumtzis D.State space reconstruction parameters in the analysis of chaotic time series-the role of the time window length [J].Physica:D,1996,95:13-28
    [7] 杨金芳,翟永杰,王东风,等.基于支持向量回归的时间序列预测[J].中国电机工程学报,2005,25(17):10-114 Yang Jinfang,Zhai Yongjie,Wang Dongfeng,et al.Time series prediction based on support vector regression [J].Proceedings of the CSEE,2005,25(17):110-114 (in Chinese)
    [8] Platt J.Fast training of support vector machines using sequential minimal optimization [M].Cambridge,MA:MIT Press,1999:185-208
    [9] 王书舟,伞冶,张允昌.基于支持向量机改进SMO算法的直升机旋翼自转着陆过程建模[J].航空学报,2009,30(1):46-51 Wang Shuzhou,San Ye,Zhang Yunchang.Modeling for landing process of helicopter with rotator self-rotating based on modified SMO algorithm of support vector machine [J].Acta Aeronautica et Astronautica Sinica,2009,30(1):46-51 (in Chinese)
    [10] 王杰,姜念,张毅.SVM算法的区间自适应PSO优化及其应用[J].郑州大学学报,2011,32(1):75-79 Wang Jie,Jiang Nian,Zhang Yi.SVM algorithm based on interval adaptive PSO and its application [J].Journal of Zhengzhou University,2011,32(1):75-79 (in Chinese)
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出版历程
  • 收稿日期:  2012-06-07
  • 网络出版日期:  2012-10-30

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