微特电机 ›› 2019, Vol. 47 ›› Issue (6): 24-30.doi: 1004-7018(2019)06-0024-07

• 设计分析 • 上一篇    下一篇

感应电动机转子偏心故障诊断方法研究

邵思语   

  1. 华南理工大学电力学院,广州 510641
  • 收稿日期:2018-05-18 出版日期:2019-06-28 发布日期:2019-07-01
  • 作者简介:邵思语(1995—),女,硕士,研究方向为电机故障的在线监测与故障诊断。

Research on Rotor Eccentricity Fault Diagnosis Methods of Induction Motor

SHAO Si-yu   

  1. School of Electirc Power, South China University of Technology,Guangzhou 510641,China
  • Received:2018-05-18 Online:2019-06-28 Published:2019-07-01

摘要:

主要研究了三种感应电机转子偏心故障诊断方法。在基于定子电流频谱法中,指出了应用常规FFT的不足和小波包分析的优势,提出改进的小波包分析结合FFT的信号分析法,并应用MATLAB处理电流信号进行验证。在不平衡磁拉力分析法中,通过理论推导建立径向力波阶次及相关频率的故障特征,并以一台感应电动机为例在ANSYS中仿真分析正常、动偏心、静偏心三种状态下的径向电磁力特性,并对它的空间和时间分布进行傅里叶分解,获得力波阶次和频率分布状况。电机转子偏心故障引起的异常振动主要取决于电机的径向电磁力波,仿真结果为振动信号分析法中提出的特征频率提供理论依据。

关键词: 感应电动机, 转子偏心, 故障特征, 定子电流频谱, 小波包分析, 径向电磁力, 振动信号

Abstract:

Three diagnostic methods of rotor eccentricity fault of induction motor were studied. In the stator current spectrum method, the shortcomings of applying conventional FFT and the advantage of the wavelet packet analysis were pointed out. The improved wavelet packet analysis combined with FFT signal analysis method was proposed, and MATLAB was used to process current signals for verification. In the unbalanced magnetic pull analysis method, the fault characteristics of the radial force wave order and the relevant frequency are established by theoretical deduction, and the radial electromagnetic force characteristics of the normal, static eccentricity and dynamic eccentricity states were simulated and analyzed in ANSYS with an induction motor as example, and Fourier decomposition of the spatial and temporal distribution was conducted to obtain the force wave order and frequency distribution. The abnormal vibration caused by the fault of the rotor eccentricity mainly depends on the radial electromagnetic force wave, the simulation results provide the theoretical basis for the characteristic frequency proposed in the vibration signal analysis method.

Key words: induction motor, rotor eccentricity, fault feature, stator current spectrum, wavelet packet analysis, radial electromagnetic forces, vibration signal

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