Effective Data-driven Method Based on Bayesian Approach for Performance Estimation of Wound-field Motors

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: 2024/05/01

タイトル(英語): Effective Data-driven Method Based on Bayesian Approach for Performance Estimation of Wound-field Motors

著者名: Tie yang Zhao (Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology), Yuki Hidaka (Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology), Shingo Hiruma (Graduate

著者名(英語): Tie yang Zhao (Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology), Yuki Hidaka (Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology), Shingo Hiruma (Graduate School of Engineering, Kyoto University), Hiroyuki Kaimori (Science Solutions International Laboratory, Inc.)

キーワード: neural network，data-driven，wound-field motor，Bayesian approach

要約(英語): This study proposes a novel data-driven method based on Bayesian approach. A d/q axis flux map and the non-linear torque characteristics of wound-field motors were estimated using a single layer neural network to reduce the number of finite element analysis and load tests. Moreover, to improve estimation accuracy even with a small number of learning data, training data-set to be input into machine learning were selected based on a Bayesian approach. The proposed method improves the estimation accuracy compared to that of the conventional data-driven method. The proposed method is validated by applying it to the numerical and experimental problem. Moreover, estimated results are compared with those of the conventional methods.

本誌: IEEJ Journal of Industry Applications Vol.13 No.3 （2024）

本誌掲載ページ: 338-345 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/13/3/13_23012206/_article/-char/ja/