Minor Bearing Fault Detection of Induction Motor - A Comparative study between Support Vector Machine and Artificial Intelligence -

カテゴリ: 全国大会

論文No: 4-261

グループ名: 【全国大会】平成31年電気学会全国大会論文集

発行日: 2019/03/01

タイトル(英語): Minor Bearing Fault Detection of Induction Motor - A Comparative study between Support Vector Machine and Artificial Intelligence -

著者名: Santhosh Gunasekaran(Nagoya Institute of Technology),Shrinathan Esakimuthu Pandarakone(Nagoya Institute of Technology),Keisuke Asano(Nagoya Institute of Technology),Yukio Mizuno(Nagoya Institute of Technology),Hisahide Nakamura(TOENEC)

著者名(英語): Santhosh Gunasekaran(Nagoya Institute of Technology),Shrinathan Esakimuthu Pandarakone(Nagoya Institute of Technology),Keisuke Asano(Nagoya Institute of Technology),Yukio Mizuno(Nagoya Institute of Technology),Hisahide Nakamura(TOENEC Corporation)

キーワード: 誘導電動機,ベアリング,故障,診断,マシンラーニング,人工知能

要約(日本語): Minor bearing fault detection of induction motor in industry becomes indispensable in the recent years. To perform maintenance activities, initial detection and follow-up the progress of fault is required to avoid financial crises. In the present paper, a comparative study is proposed between Support Vector Machine (SVM) and artificial intelligence - deep learning (DL) for detecting minor fault in bearing. DL produced high accuracy rate than SVM due to the neural networks which provides better feedback during the training and detection phase. The proposed method is effective in detecting the minor bearing fault at the initial stage.

原稿種別: 英語

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