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首页> 外文会议>International Symposium on Computer, Consumer and Control >Rotor Fault Identification of Induction Motor Based on Discrete Fractional Fourier Transform
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Rotor Fault Identification of Induction Motor Based on Discrete Fractional Fourier Transform

机译：基于离散分数阶傅里叶变换的异步电动机转子故障识别

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摘要

While the rotor of a variable frequency induction motor malfunctions, the signal change of stator current will reflect this fault. This paper proposes an identification method of rotor fault for induction motor based on discrete fractional Fourier transform of stator current. The induction motor's three-phase stator current is firstly measured and then discrete fractional Fourier transform is utilized to identify the rotor's fault. Discrete fractional Fourier transform can transform time domain signal at any angle. This study divides angles 0~2π into 20 equal parts. Each part of angle can then be sequentially transformed to build a characteristic matrix. In order to make the characteristic information more identifiable, fractal theory is used to extract two features of fractal dimension and lacunarity from characteristic matrix. Finally, extension theory is applied to identify the defective type. In the identification process, ±5%~±15% random noise is added to evaluate the anti-interference ability of the proposed method. To verify the effectiveness three defective models are specially built according to common induction motor faults, including three broken rotor bar, one hollow rotor bar, and bearing ball damage. Normal motor is also added for identification analysis. The test results show that the identification method can effectively identify rotor fault type and has the advantage of strong anti-interference.
机译：当变频感应电动机的转子发生故障时，定子电流的信号变化将反映该故障。提出了一种基于定子电流离散分数阶傅里叶变换的感应电动机转子故障识别方法。首先测量感应电动机的三相定子电流，然后利用离散分数阶傅里叶变换来识别转子的故障。离散分数阶傅里叶变换可以在任何角度变换时域信号。本研究将角度0〜2π分为20个相等的部分。然后可以依次变换角度的每个部分以构建特征矩阵。为了使特征信息更加可识别，分形理论被用于从特征矩阵中提取分形维数和隐隐性的两个特征。最后，将扩展理论应用于识别缺陷类型。在识别过程中，加入±5％〜±15％的随机噪声，以评价该方法的抗干扰能力。为了验证有效性，根据常见的感应电动机故障专门构建了三个有缺陷的模型，包括三个损坏的转子排，一个空心转子排和轴承滚珠损坏。还添加了普通电动机以进行识别分析。测试结果表明，该识别方法能够有效识别转子故障类型，具有抗干扰能力强的优点。

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