基于SMO-SVR的飞机舵面损伤故障趋势预测

董磊; 任章; 李清东

基于SMO-SVR的飞机舵面损伤故障趋势预测

北京航空航天大学飞行器控制一体化技术重点实验室, 北京 100191

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
计量
- 文章访问数: 2120
- HTML全文浏览量: 171
- PDF下载量: 562
- 被引次数: 0
出版历程
- 收稿日期: 2012-06-07
- 网络出版日期: 2012-10-30

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

Science and Technology on Aircraft Control Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

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τ_d.根据所求m和τ_d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.
- 故障趋势预测 /
- 支持向量回归 /
- 序贯最小优化 /
- 舵面损伤 /
- 相空间重构
Abstract: In order to predict changes more accurately when the surface of aircraft damaged, an algorithm based on improved sequential minimal optimization support vector regression (SMO-SVR) was presented. This algorithm reconstructed the phase space of multivariate and nonlinear time series using improved C-C average method to determine the embedding dimension m and the delay time τ_d. Then, a weighted SVR model was built according to m and τ_d, and in which the halt criterion of SMO was modified. The parameters of SVR were optimized by interval adaptive particle swarm optimization (IAPSO) to improve the efficiency of parameter optimization. In order to verify the validity of the algorithm, the prediction and analysis of surface damage trend were performed. Comparing with the radial basis function neural network (RBFNN) method, the simulation result demonstrates that the improved SMO-SVR prediction model has good predictive ability.
- fault prediction /
- support vector regression /
- sequential minimal optimization /
- surface damage /
- phase space reconstruction

HTML全文

参考文献(10)

[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)