電気学会論文誌D(産業応用部門誌)
Online ISSN : 1348-8163
Print ISSN : 0913-6339
ISSN-L : 0913-6339
論文
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
近藤 稔
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ジャーナル 認証あり

2019 年 139 巻 2 号 p. 199-205

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抄録

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.

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