Prediction of IPM Machine Torque Characteristics Using Deep Learning Based on Magnetic Field Distribution
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 accur...
Main Authors: | Hidenori Sasaki, Yuki Hidaka, Hajime Igarashi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9786828/ |
Similar Items
-
Analysis of the Saliency Ratio Effect on the Output Torque and the System Efficiency in IPM Drives
by: Mikail Koç, et al.
Published: (2021-12-01) -
Putting IPM back into Citrus
by: Lukasz L. Stelinski, et al.
Published: (2019-07-01) -
Design and Optimization of Interior Permanent Magnet (IPM) Motor for Electric Vehicle Applications
by: Lavanya Balasubramanian, et al.
Published: (2023-06-01) -
Wide-Speed Range Sensorless Control of an IPM Motor for Multi-Purpose Applications
by: Maria Laura Bacci, et al.
Published: (2020-06-01) -
Design and Analysis of a New Topology of Rotor Magnets in Brushless DC Motors to Reduce Cogging Torque
by: Seyed Reza Mousavi-Aghdam, et al.
Published: (2021-01-01)