Performance Prediction of Electric Motors via Deep Learning

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門（英文）

発行日: 2023/03/01

タイトル(英語): Performance Prediction of Electric Motors via Deep Learning

著者名: Masatsugu Oyamada (Nagasaki Factory, Toshiba Mitsubishi-Electric Industrial Systems Co.), Sadaaki Kunimatsu (Kumamoto University), Ikuro Mizumoto (Kumamoto University)

著者名(英語): Masatsugu Oyamada (Nagasaki Factory, Toshiba Mitsubishi-Electric Industrial Systems Co.), Sadaaki Kunimatsu (Kumamoto University), Ikuro Mizumoto (Kumamoto University)

キーワード: deep learning，neural network，electric motor，performance prediction，practical use

要約(英語): When designing electric motors, many types of performances (electrical and mechanical characteristics) must be predicted with good accuracy. In general, these performances are determined based on complex theoretical calculations, but theoretical calculations include various assumptions. Therefore, it is difficult to eliminate prediction errors when predicting performance, and it is necessary to improve accuracy by referring actual test data. Recently, with the digitalization of the manufacturing process, a large amount of actual data has been converted into a database, and it is expected to be put to effective use. Here, a neural network that predicts various performances of electric motors using a large amount of actual data as a training dataset, is constructed to achieve uniform and high-precision performance prediction via deep learning. Its practical use for actual design work is verified in this study.

本誌: IEEJ Journal of Industry Applications Vol.12 No.2 （2023） Special Issue on “Motion Control and its Related Technologies”

本誌掲載ページ: 238-243 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/12/2/12_22005304/_article/-char/ja/