基于混沌吸引子的飞轮故障检测

李磊; 高永明; 吴止锾

doi:10.13700/j.bh.1001-5965.2018.0037

基于混沌吸引子的飞轮故障检测

doi: 10.13700/j.bh.1001-5965.2018.0037

航天工程大学航天信息学院, 北京 101416

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
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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

Fault detection for flywheels based on chaotic attractor

School of Space Information, Space Engineering University, Beijing 101416, China

More Information

Corresponding author: GAO Yongming, E-mail:YongmingGao_08@163.com

摘要

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。
- 飞轮 /
- 混沌吸引子 /
- 曲面迭代 /
- 故障检测 /
- radon变换
Abstract:
Aimed at the problem that the early fault of the flywheel is difficult to detect and the precision mathematical model is difficult to be established, a fault detection method based on the characteristics of chaotic attractor is proposed. This method uses the auxiliary curved surface function and the system parameters to construct the discrete dynamical system. The approximate chaotic attractors obtained from normal data through iteration are different with the ones obtained from fault data. The difference could be used as feature for fault detection. The simulation results show that the discrete dynamical system constructed by this method can generate the chaotic attractor stably. The chaotic attractor is independent of the initial iteration point. The same faults under different working conditions have the same characteristics. The chaotic attractor feature is sensitive to small fault.
- flywheels /
- chaotic attractor /
- curved surface iteration /
- fault detection /
- radon transform

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图 1 高精度飞轮仿真模型

Figure 1. High-accuracy simulation model of flywheel

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图 2 I_m的最大Lyapunov指数

Figure 2. The largest Lyapunov exponent of I_m

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图 3 离散动力系统的分岔图

Figure 3. Bifurcation diagram of discrete dynamic system

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图 4 离散动力系统的最大Lyapunov指数

Figure 4. The largest Lyapunov exponent of discrete dynamic system

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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)

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图 6 初始点(65, 90)，K_gd=0.7时的混沌吸引子

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

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图 7 闭环飞轮控制系统框图

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

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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

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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

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图 10 不同噪声条件下电机增益变小故障检测结果(K_gd=0.7)

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

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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

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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

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图 13 不同噪声条件下摩擦力矩增大故障检测结果(K_τ=1.3)

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

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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

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图 15 不同m下电机故障电流均值

Figure 15. Mean of fault motor current under different m

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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G_d/(A·V^-1)	1
电机转动系数K_t/(N·m·A^-1)	0.19
电机电动势反馈系数K_e/(V·rad^-1·s^-1)	0.29
转速限制增益系数K_s/(V·rad^-1·s^-1)	95
转速限制阈值ω_s/ (r·min^-1)	690
静摩擦力矩τ_c/(N·m)	0.002
飞轮转动惯量J/(N·m·s²)	0.078
电机磁极数量N_p	36
滑动摩擦力矩τ_v/(N·m·s·rad^-1)	0.037
输入电阻R_in/Ω	2
力矩噪声引起的角误差θ_α/rad	0.05
高通噪声滤波器频率ω_α/(rad·s^-1)	0.2
电压反馈增益k_f	0.5

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