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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
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出版历程
  • 收稿日期:  2018-01-12
  • 录用日期:  2018-05-11
  • 刊出日期:  2018-09-20

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