基于改进ACGAN的永磁同步电机数据扩张方法

永磁同步电机（permanent magnet synchronous motor,PMSM）的监测数据呈现出非平稳、非线性、多源异构性和价值低密度性等特点,而仿真数据难以准确地模拟电机故障类型和故障程度,使得正常数据与故障数据的样本呈现严重不均衡现象,导致故障诊断的模型训练容易出现过拟合、精度低等问题。本文提出了一种改进辅助分类生成对抗网络（auxiliary classification generation adversarial network,ACGAN）,通过对原始样本的分布特性进行学习,实现对PMSM实测故障数据的扩张,为电机的故障诊断和健康评估提供数据基础。首先,针对ACGAN网络收敛性差和梯度易消失或爆炸的问题,使用Wasserstein距离约束生成数据的重建损失,利用梯度惩罚代替权值剪裁对模型进行优化,解决模型训练不稳定问题；其次,剖析数据之间的变化关系和历史变化规律,在生成器中引入循环神经网络提高生成数据质量；最后,利用PMSM匝间短路的故障数据,对比分析ROS、SMOTE、ADASYN及改进ACGAN 4种数据扩张方法对提升故障诊断模型性能的有效性。分析结果表明,与其他数据扩张方法相比,改进ACGAN方法的模型训练较稳定、收敛速度较快,扩张数据质量较高。

Data expansion method of permanent magnet synchronous motor based on improved ACGAN

The monitoring data of permanent magnet synchronous motor (PMSM) exhibit complexities such as non-smoothness, non-linearity, multi-source heterogeneity and low value density. These characteristics make it challenging to accurately model the type and extent of motor faults using simulation data. The serious imbalance between normal and faulty data samples leads to problems such as overfitting and low accuracy in the training of fault diagnosis models. In this paper, an improved auxiliary classification generation adversarial network (ACGAN) is proposed to study the expansion of real fault data for PMSM by learning the distribution characteristics of the original samples, while the generated fault dataset provides a data base for the next fault diagnosis and health assessment. Firstly, to address the problems of poor convergence and the tendency for gradients to disappear or explode in ACGAN networks, the Wasserstein distance is used to constrain the reconstruction loss of the generated data, and the gradient penalty is used instead of weight clipping to optimize the model and mitigate model training instability. Secondly, to analyze the change relationship between data and the historical change pattern, recurrent neural network is introduced in the generator to improve the quality of the generated data. Finally, the effectiveness of four data expansion methods, ROS, SMOTE, ADASYN and improved ACGAN, is compared and analyzed in improving the performance of fault diagnosis models using fault data from PMSM inter-turn short circuits. Results show that the model trained using the improved ACGAN method is more stable, converges faster and produces expanded data of superior quality than those adopting other data expansion methods.