The diagnosis of permanent magnet synchronous motor (PMSM) faults has been the subject of much research in recent years, due to the growing reliability and safety requirements for drive systems. This article concerns PMSM stator winding fault detection and classification. A novel intelligent diagnosis approach is proposed, based on the bispectrum analysis of a stator phase current and the convolutional neural network (CNN). Rather than using raw phase current signals, bispectrum is applied for symptom extraction and utilized as the input for a pretrained CNN model. The CNN model is used for automatic inference on the winding condition of the PMSM stator. Experimental results are presented to validate the proposed algorithm. The classification effectiveness of the developed CNN is as high as 99.4%. This article also presents the possibility of improving the accuracy of the CNN model and reducing the training time by properly tuning the training parameters. The CNN model learning time is only about one minute. The fault classifier model is developed in Python programming language, avoiding the cost of purchasing additional software.

中文翻译：

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基于双谱分析和卷积神经网络的PMSM定子绕组故障检测与分类


由于对驱动系统的可靠性和安全性要求不断提高，永磁同步电机（PMSM）故障诊断近年来已成为大量研究的主题。本文涉及 PMSM 定子绕组故障检测和分类。提出了一种基于定子相电流双谱分析和卷积神经网络（CNN）的新型智能诊断方法。不是使用原始相电流信号，而是应用双谱进行症状提取并用作预训练 CNN 模型的输入。 CNN模型用于自动推断PMSM定子的绕组状况。实验结果验证了所提出的算法。所开发的CNN的分类效率高达99.4%。本文还提出了通过适当调整训练参数来提高 CNN 模型的准确性并减少训练时间的可能性。 CNN模型的学习时间只有一分钟左右。故障分类器模型采用Python编程语言开发，避免了购买额外软件的成本。