最新の機械学習法を用いた回転機の異常監視手法の提案
英字タイトル:
Proposal of Rotating Machine Health Monitoring Based on Modern Machine Leaning Methods
著者:
渡邉 将也 Masaya WATANABE 吉村 哲平 Teppei YOSHIMURA 兼本 茂 Shigeru KANEMOTO
発刊日:
公開日:
Deep leaning neural networkMachine learningRotating machine monitoringSupport vector machine
Proposal of Rotating Machine Health Monitoring Based on Modern Machine Leaning Methods
著者:
渡邉 将也 Masaya WATANABE 吉村 哲平 Teppei YOSHIMURA 兼本 茂 Shigeru KANEMOTO
発刊日:
公開日:
カテゴリ: 第11回
キーワードタグ:Deep leaning neural networkMachine learningRotating machine monitoringSupport vector machine
概要
Abstract The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitorin g. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mock up test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eighen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.
