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Prediction of IPM Machine Torque Characteristics Using Deep Learning Based on Magnetic Field Distribution
| Title: | Prediction of IPM Machine Torque Characteristics Using Deep Learning Based on Magnetic Field Distribution |
| Authors: | Sasaki, Hidenori Browse this author | | Hidaka, Yuki Browse this author | | Igarashi, Hajime Browse this author →KAKEN DB |
| Keywords: | Optimization | | Magnetic resonance imaging | | Torque | | Topology | | Finite element analysis | | Saturation magnetization | | Permanent magnet motors | | Topology optimization | | deep learning | | IPM motor | | finite element method |
| Issue Date: | 2-Jun-2022 |
| Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
| Journal Title: | IEEE Access |
| Volume: | 10 |
| Start Page: | 60814 |
| End Page: | 60822 |
| Publisher DOI: | 10.1109/ACCESS.2022.3179835 |
| Abstract: | 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. |
| Rights: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/86476 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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