Prediction of IPM Machine Torque Characteristics Using Deep Learning Based on Magnetic Field Distribution
Prediction of IPM Machine Torque Characteristics Using Deep Learning Based on Magnetic Field Distribution
复制标题
基于磁场分布的深度学习预测IPM机器扭矩特性
DOI：
10.1109/access.2022.3179835
复制
发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Igarashi Hajime
中科院分区：
文献类型：
--
作者：
Sasaki Hidenori;Hidaka Yuki;Igarashi Hajime
本文提出了一种使用深度神经网络（DNN）准确预测旋转机械性能的新方法。在该方法中，在固定机械角度的旋转机器的横截面上的磁场分布被用作DNN的输入数据。比较了由磁通密度分布和材料结构训练的神经网络对内置式永磁电机转矩特性的预测精度。结果表明，所提出的方法有利于更准确地预测机器的性能比传统的方法，其中旋转机器的横截面图像输入到DNN。此外，DNN的学习所提出的方法被应用到拓扑优化算法。该方法可以有效地加快拓扑优化的速度，因为它可以减少有限元分析的次数。与传统的无代理模型优化方法相比，总的计算量减少了52.5%。
This paper proposes a new method for accurately predicting rotating machine properties using a deep neural network (DNN). In this method, the magnetic field distribution over a cross-section of a rotating machine at a fixed mechanical angle is used as the input data for the DNN. The prediction accuracy of the torque properties of an inner permanent magnet (IPM) motor for the CNNs trained by the magnetic flux density distribution and material configuration is compared. It is shown that the proposed method facilitates a more accurate prediction of machine performance than a conventional method in which the cross-sectional image of a rotating machine is input to the DNN. Furthermore, the DNN learned by the proposed method is applied to the topology optimization algorithm. Topology optimization can be effectively accelerated because the number of analyses by the finite element method can be reduced using the proposed method. The total computing cost is reduced by 52.5% compared with conventional optimization without surrogate models.