基于GAN-DRSN的PMSM匝间短路故障诊断

Ming Li, Manyi Wang, Longmiao Chen, Liuxuan Wei
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引用次数: 0

摘要

由于当前永磁同步电机匝间短路故障采样数量少,且电机工作在高噪声环境中,采集到的数据包含复杂的噪声。首先在大数据模拟数据集上对深度残差收缩网络进行预训练。然后为了避免真实数据集之间的不平衡,本文采用GAN网络生成更多的数据集。在上述数据集的基础上,提出预训练网络对数据集中的环境和其他噪声进行去噪。在网络中引入了空间Dropout层,提高了故障诊断的精度和收敛速度。实验表明,将GAN和DRSN相结合的方法用于不平衡样本的故障诊断,可以有效地平衡数据集等干扰和降低环境噪声。诊断准确率高达97.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GAN-DRSN based Inter-turn Short Circuit Fault Diagnosis of PMSM
Due to the small number of samples of the inter-turn short-circuit fault of the current permanent magnet synchronous motor, and the motor working in a high-noise environment, the collected data contains complicated noise. So first the deep residual shrinkage network is pre-trained on the big data simulation dataset. And then to avoid imbalances between real data sets, GAN network is adopted to generate more datasets in this paper. Based on the aforementioned data set, the pretrained network is proposed to denoise the environment and other noise in the data set. And Spatial Dropout layer into the network is introduced to improve the accuracy and convergence speed of fault diagnosis. Experiments show that by combining GAN and DRSN methods for fault diagnosis of unbalanced samples, disturbances such as datasets and reducing environmental noise can be effectively balanced. The diagnostic accuracy is as high as 97.5%.
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