无刷直流电机匝间短路故障定位及定量评估方法研究
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国家自然科学基金(52075002)


Localization and evaluation method of interturn short circuit fault in BLDC motor
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    摘要:

    针对无刷直流电机匝间短路故障问题,提出一种结合深度迁移学习和多维特征拟合方法,以实现匝间短路故障的精确定位和定量评估。同步采集电机定子绕组的三相电流信号,将一维电流信号转化为图像信号,采用基于迁移学习的卷积神经网络实现匝间短路故障的定位,在确定故障相之后,从电流信号中提取并筛选敏感特征,采用特征拟合方法实现故障等级的定量评估。实验结果表明,所提出的方法能够实现100%精度的故障相定位,同时故障定量评估的相对平均误差低至4.33%。该方法对于永磁电机系统的定子绕组故障精确定位和精密诊断具有潜在的应用价值。

    Abstract:

    Brushless direct current (BLDC) motors have been widely used in industry and factory automations, and electric vehicles. Interturn short circuit fault is one of the dominated faults for a BLDC motor, and this fault affects precision control, induces noise and vibration, and even causes motor burn down and fires. Hence, diagnosis of interturn short circuit fault of BLDC motor is of significance. This paper proposes a method that combines of transfer learning and features fitting to realize accurate fault localization and evaluation. First, the three-phase current signals of the motor stator windings are synchronously sampled. The one-dimensional current signals are transformed to an image, and then a transfer learning-based convolutional neural networks model is trained for fault localization. When the fault phase has been localized, the sensitive features are extracted and selected from the corresponding phase current, and then features fitting method is designed to qualitative evaluate the fault levels. Experimental results indicate that the proposed method can localize the faults with accuracy of 100%, and the relative average error of fault quantitative assessment is 4.33%. The proposed method shows potential applications for accurate localization and evaluation of stator winding faults in permanent magnet motor systems.

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王吉亮,王 慧,王骁贤,陆思良.无刷直流电机匝间短路故障定位及定量评估方法研究[J].河北科技大学学报,2021,42(3):248-256

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  • 收稿日期:2021-04-07
  • 最后修改日期:2021-05-07
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  • 在线发布日期: 2021-07-08
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