機械学習による車両機器状態監視のための代表データ選択法

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2019/02/01

タイトル(英語): Prototype Selection Method for Vehicle Condition Monitoring using Machine Learning

著者名: 近藤　稔（鉄道総合技術研究所）

著者名(英語): Minoru Kondo (Railway Technical Research Institute)

キーワード: 代表データ選択，機械学習，状態監視，鉄道車両，近傍法　　prototype selection，machine learning，condition monitoring，railway vehicle，nearest neighbor

要約(英語): The authors are developing a condition monitoring system using vibration analysis and machine learning for the purpose of monitoring the condition of railway vehicle equipment. In railway vehicles, vibrations change due to long-term state change, so long-term data should be used for learning. In this case, it is not practical to use all data, so it is necessary to use only some part of the data, which is called prototype data. Therefore, a prototype selection method based on the neighborhood method is proposed in this paper. As a result of applying the proposed method to the vibration data during the abnormal simulation test, the expected effect was confirmed.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.139 No.2 （2019） 特集：ドローンとロボット組み込み/サスティナブルシステム

本誌掲載ページ: 199-205 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/139/2/139_199/_article/-char/ja/