留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于混沌吸引子的飞轮故障检测

李磊 高永明 吴止锾

李磊, 高永明, 吴止锾等 . 基于混沌吸引子的飞轮故障检测[J]. 北京航空航天大学学报, 2018, 44(9): 1894-1902. doi: 10.13700/j.bh.1001-5965.2018.0037
引用本文: 李磊, 高永明, 吴止锾等 . 基于混沌吸引子的飞轮故障检测[J]. 北京航空航天大学学报, 2018, 44(9): 1894-1902. doi: 10.13700/j.bh.1001-5965.2018.0037
LI Lei, GAO Yongming, WU Zhihuanet al. Fault detection for flywheels based on chaotic attractor[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1894-1902. doi: 10.13700/j.bh.1001-5965.2018.0037(in Chinese)
Citation: LI Lei, GAO Yongming, WU Zhihuanet al. Fault detection for flywheels based on chaotic attractor[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1894-1902. doi: 10.13700/j.bh.1001-5965.2018.0037(in Chinese)

基于混沌吸引子的飞轮故障检测

doi: 10.13700/j.bh.1001-5965.2018.0037
详细信息
    作者简介:

    李磊  男, 博士研究生。主要研究方向:故障诊断、数据挖掘、机器学习

    高永明  男, 博士, 副教授。主要研究方向:计算机仿真、复杂系统建模

    吴止锾  男, 博士研究生。主要研究方向:图像处理、数据挖掘、机器学习

    通讯作者:

    高永明, E-mail:YongmingGao_08@163.com

  • 中图分类号: V474

Fault detection for flywheels based on chaotic attractor

More Information
  • 摘要:

    针对飞轮早期故障难以检测、精确数学模型难以建立的问题,提出一种基于混沌吸引子特征的故障检测方法。该方法利用辅助曲面函数与系统参量构造离散动力系统,通过迭代产生近似混沌吸引子,正常数据与故障数据所产生的混沌吸引子形态不同,以此为特征进行故障检测。仿真结果表明,该方法构造的离散动力系统能够稳定地产生混沌吸引子;产生的混沌吸引子与初始迭代点无关;同种故障在不同工况下的特征相同;混沌吸引子特征对微小幅度的故障敏感。

     

  • 图 1  高精度飞轮仿真模型

    Figure 1.  High-accuracy simulation model of flywheel

    图 2  Im的最大Lyapunov指数

    Figure 2.  The largest Lyapunov exponent of Im

    图 3  离散动力系统的分岔图

    Figure 3.  Bifurcation diagram of discrete dynamic system

    图 4  离散动力系统的最大Lyapunov指数

    Figure 4.  The largest Lyapunov exponent of discrete dynamic system

    图 5  初始点(65, 90)和(90,65)的混沌吸引子

    Figure 5.  Chaotic attractor with initial points (65, 90) and (90, 65)

    图 6  初始点(65, 90),Kgd=0.7时的混沌吸引子

    Figure 6.  Chaotic attractor with initial point (65, 90) and Kgd=0.7

    图 7  闭环飞轮控制系统框图

    Figure 7.  Block diagram of flywheel with closed-loop control system

    图 8  飞轮电机增益变小故障的电机电流

    Figure 8.  Current of flywheel under fault of motor gain decrease

    图 9  75 dB噪声下电机增益变小故障检测结果

    Figure 9.  Fault detection result of motor gain decrease under 75 dB noise

    图 10  不同噪声条件下电机增益变小故障检测结果(Kgd=0.7)

    Figure 10.  Fault detection result of motor gain decrease under different noises (Kgd=0.7)

    图 11  摩擦力矩增大故障的的电机电流

    Figure 11.  Current of motor in fault of motor friction moment increase

    图 12  75 dB噪声下摩擦力矩增大故障检测结果

    Figure 12.  Fault detection result of motor friction moment increase under 75 dB noise

    图 13  不同噪声条件下摩擦力矩增大故障检测结果(Kτ=1.3)

    Figure 13.  Fault detection result of motor friction moment increase under different noises (Kτ=1.3)

    图 14  不同m下电机摩擦力矩增大故障检测结果

    Figure 14.  Fault detection result of motor friction moment increase with different m

    图 15  不同m下电机故障电流均值

    Figure 15.  Mean of fault motor current under different m

    表  1  飞轮仿真模型参数设置

    Table  1.   Parameter setting of flywheel simulation model

    参数 数值
    驱动增益Gd/(A·V-1) 1
    电机转动系数Kt/(N·m·A-1) 0.19
    电机电动势反馈系数Ke/(V·rad-1·s-1) 0.29
    转速限制增益系数Ks/(V·rad-1·s-1) 95
    转速限制阈值ωs/ (r·min-1) 690
    静摩擦力矩τc/(N·m) 0.002
    飞轮转动惯量J/(N·m·s2) 0.078
    电机磁极数量Np 36
    滑动摩擦力矩τv/(N·m·s·rad-1) 0.037
    输入电阻Rin 2
    力矩噪声引起的角误差θα/rad 0.05
    高通噪声滤波器频率ωα/(rad·s-1) 0.2
    电压反馈增益kf 0.5
    下载: 导出CSV
  • [1] 肇刚, 李泽, 李言俊.基于TSEOPM的在轨航天器故障预报方法研究[J].计算机测量与控制, 2009, 17(12):2352-2354. http://d.old.wanfangdata.com.cn/Periodical/jsjzdclykz200912003

    ZHAO G, LI Z, LI Y J.Research on method of fault prediction for onboard spacecrafts based on times series event omen pattern mining[J].Computer Meassurement & Control, 2009, 17(12):2352-2354(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jsjzdclykz200912003
    [2] 苏林, 尚朝轩, 刘文静.航天器姿态控制系统故障诊断方法概述[J].长春理工大学学报(自然科学版), 2010, 33(4):23-27. doi: 10.3969/j.issn.1672-9870.2010.04.006

    SU L, SHANG C X, LIU W J.Survey on the technology of fault diagnosis for spacecraft attitude control system[J].Journal of Changchun University of Science and Technology(Natural Science Edition), 2010, 33(4):23-27(in Chinese). doi: 10.3969/j.issn.1672-9870.2010.04.006
    [3] GAO C, DUAN G R.Robust adaptive fault estimation for a class of nonlinear systems subject to multiplicative faults[J].Circuits, Systems, and Signal Processing, 2012, 31(6):2035-2046. doi: 10.1007/s00034-012-9434-x
    [4] 管宇, 张迎春, 沈毅, 等.基于迭代学习观测器的卫星姿态控制系统的鲁棒容错控制[J].宇航学报, 2012, 33(8):1080-1086. doi: 10.3873/j.issn.1000-1328.2012.08.012

    GUAN Y, ZHANG Y C, SHEN Y, et al.Robust fault-tolerant control for satellite attitude control system based on iterative learning observer[J].Journal of Astronautics, 2012, 33(8):1080-1086(in Chinese). doi: 10.3873/j.issn.1000-1328.2012.08.012
    [5] 李知周, 张锐, 朱振才, 等.基于扩展卡尔曼滤波的动量轮故障检测方法[J].航空学报, 2010, 31(8):1614-1621. http://d.old.wanfangdata.com.cn/Periodical/hkxb201008016

    LI Z Z, ZHANG R, ZHU Z C, et al.Extended Kalman filter-based fault detection for momentum wheel[J].Acta Aeronautica et Astronautica Sinica, 2010, 31(8):1614-1621(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hkxb201008016
    [6] AZIZI S M, KHORASANI K.Adistributed Kalman filter for actuator fault estimation of deep space formation flying satellites[C]//Proceedings of 20093rd Annual IEEE Systems Conference.Piscataway, NJ: IEEE Press, 2009: 354-359. https://www.mendeley.com/research-papers/distributed-kalman-filter-actuator-fault-estimation-deep-space-formation-flying-satellites/
    [7] TALEBI H A, KHORASANI K.A neural network-based multiplicative actuator fault detection and isolation of nonlinear systems[J].IEEE Transactions on Control Systems Technology, 2013, 21(3):842-851. doi: 10.1109/TCST.2012.2186634
    [8] SHEN Q K, JIANG B, SHI P.Novel neural networks-based fault tolerant control scheme with fault alarm[J].IEEE Transactions on Cybernetics, 2014, 44(11):2190-2201. doi: 10.1109/TCYB.2014.2303131
    [9] 高运广, 王仕成, 刘志国, 等.一种基于LS-SVM的联邦滤波故障检测方法[J].控制与决策, 2011, 26(9):1433-1440. http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201109031.htm

    GAO Y G, WANG S C, LIU Z G, et al.Fault detection method based on LS-SVM for federated Kalman filter[J].Control and Decision, 2011, 26(9):1433-1440(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201109031.htm
    [10] 陈冰, 鲁刚, 房红征, 等.基于最小二乘支持向量机的卫星异常检测方法[J].计算机测量与控制, 2014, 22(3):690-696. doi: 10.3969/j.issn.1671-4598.2014.03.014

    CHEN B, LU G, FANG H Z, et al.Method of satellite anomaly detection based on least squares support vector machine[J].Computer Measurement & Control, 2014, 22(3):690-696(in Chinese). doi: 10.3969/j.issn.1671-4598.2014.03.014
    [11] 王日新, 龚学兵, 许敏强, 等.飞轮系统的符号动力学故障检测方法[J].哈尔滨工业大学学报, 2016, 48(10):31-38. doi: 10.11918/j.issn.0367-6234.2016.10.004

    WANG R X, GONG X B, XU M Q, et al.A symbolic dynamic analysis of flywheel system for fault detection[J].Journal of Harbin Institute of Technology, 2016, 48(10):31-38(in Chinese). doi: 10.11918/j.issn.0367-6234.2016.10.004
    [12] 龚学兵, 王日新, 许敏强.飞轮传感器的高斯混合模型故障检测方法[J].宇航学报, 2015, 36(6):699-705. doi: 10.3873/j.issn.1000-1328.2015.06.011

    GONG X B, WANG R X, XU M Q.Gaussian mixed model-based fault detection method for flywheel sensor[J].Journal of Astronautics, 2015, 36(6):699-705(in Chinese). doi: 10.3873/j.issn.1000-1328.2015.06.011
    [13] 龚学兵, 王日新, 许敏强.基于数据关联性分析的飞轮异常检测[J].航空学报, 2015, 36(3):898-906. http://d.old.wanfangdata.com.cn/Periodical/hkxb201503023

    GONG X B, WANG R X, XU M Q.Abnormality detection for flywheel based on data association analysis[J].Acta Aeronautica et Astronautica Sinica, 2015, 36(3):898-906(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hkxb201503023
    [14] ZHANG X D, LIU X D, ZHENG Y.Chaotic dynamic behavior analysis and control for a financial risk system[J].Chinese Physics B, 2013, 22(3):260-265. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QKC20132013041700021331
    [15] TAKENS F.Detecting strange attractors in turbulence[J].Lecture Notes in Mathmatics, 1981, 898:361-381. doi: 10.1007-s12079-011-0121-7/
    [16] 孙自强, 陈长征, 谷艳玲, 等.基于混沌和取样积分技术的大型风电增速箱早期故障诊断[J].振动与冲击, 2013, 32(9):113-117. doi: 10.3969/j.issn.1000-3835.2013.09.022

    SUN Z Q, CHEN C Z, GU Y L, et al.Incipient fault diagnosis of large scale wind turbine gearbox based on chaos theory and sampling integral technology[J].Journal of Vibration and Shock, 2013, 32(9):113-117(in Chinese). doi: 10.3969/j.issn.1000-3835.2013.09.022
    [17] 翁鹤, 皮德常.混沌RBF神经网络异常检测算法[J].计算机技术与发展, 2014, 24(7):29-33. http://d.old.wanfangdata.com.cn/Periodical/wjfz201407008

    WENG H, PI D C.Chaotic RBF neural network anomaly deection algorithm[J].Computer Technology and Development, 2014, 24(7):29-33(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/wjfz201407008
    [18] 高瑞乾, 甘新年, 闫源江.基于混沌特征的航空发动机故障诊断研究[J].国外电子测量技术, 2011, 30(3):24-28. doi: 10.3969/j.issn.1002-8978.2011.03.007

    GAO R Q, GAN X N, YAN Y J.Study on aero-engine fault diagnosis based on chaotic feature[J].Foreign Electronic Measurement Technology, 2011, 30(3):24-28(in Chinese). doi: 10.3969/j.issn.1002-8978.2011.03.007
    [19] 侯胜利, 胡金海, 李应红.基于混沌变量的航空发动机性能监控与故障诊断[J].航空动力学报, 2005, 20(2):314-317. doi: 10.3969/j.issn.1000-8055.2005.02.028

    HOU S L, HU J H, LI Y H.Aeroengine performance monitoring and fault diagnosis based on chaos variable[J].Journal of Aerospace Power, 2005, 20(2):314-317(in Chinese). doi: 10.3969/j.issn.1000-8055.2005.02.028
    [20] BIALKE B.High fidelity mathematical modeling of reaction wheel performance[J].Advances in the Astronautical Sciences, 1998, 98:483-496. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=CC026077273
    [21] 于万波, 赵斌.曲面迭代混沌特性研究[J].物理学报, 2014, 63(12):120502. doi: 10.7498/aps.63.120502

    YU W B, ZHAO B.A new chaotic attractor graphics drawing method based on the curved iteration[J].Acta Physica Sinica, 2014, 63(12):120502(in Chinese). doi: 10.7498/aps.63.120502
  • 加载中
图(15) / 表(1)
计量
  • 文章访问数:  614
  • HTML全文浏览量:  147
  • PDF下载量:  273
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-01-12
  • 录用日期:  2018-05-11
  • 网络出版日期:  2018-09-20

目录

    /

    返回文章
    返回
    常见问答