A study on anomaly prediction method of machine tools - Feature extraction for anomaly prediction

Ryo Fujita, Takashi Yoshimi

研究成果: Conference contribution

抜粋

This research aims to develop an anomaly prediction method only based on sensor data acquired from machine tools. In order to realize this aim, first of all, it is necessary to extract the feature to classify whether the machine tool is in an anomalous state or normal state. This paper shows the result of examining the feature extraction method of machine tool applying distribution and chaos theory to acquired sensor data. In the distribution method, an anomalous state and a normal state are compared by creating a frequency distribution diagram using the heat map. In chaos theory, the anomalous state and the normal state are compared by reconstructing attractors using Takens' embedding theorem. In addition, quantitative evaluation using topological geometry to the anomaly prediction of machine tools is considered and its result is shown. Both methods, the distribution and chaos theory, were confirmed their features for classifying the normal state and the anomalous state. And, we also confirmed the effectiveness of the quantitative analysis of the reconstructed attractors by using the method of topological geometry.

元の言語English
ホスト出版物のタイトル2017 Asian Control Conference, ASCC 2017
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1505-1508
ページ数4
2018-January
ISBN(電子版)9781509015733
DOI
出版物ステータスPublished - 2018 2 7
イベント2017 11th Asian Control Conference, ASCC 2017 - Gold Coast, Australia
継続期間: 2017 12 172017 12 20

Other

Other2017 11th Asian Control Conference, ASCC 2017
Australia
Gold Coast
期間17/12/1717/12/20

ASJC Scopus subject areas

  • Control and Optimization

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  • これを引用

    Fujita, R., & Yoshimi, T. (2018). A study on anomaly prediction method of machine tools - Feature extraction for anomaly prediction. : 2017 Asian Control Conference, ASCC 2017 (巻 2018-January, pp. 1505-1508). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASCC.2017.8287396