生産ラインにおける異常検知・非定常サイクル同定のオンラインシステム
Author:
Affiliation:
1. Graduate School of Advanced Mathematical Sciences, Meiji University
2. Faculty of Science and Engineering, Chuo University
3. School of Interdisciplinary Mathematical Sciences, Meiji University
Publisher
The Society of Instrument and Control Engineers
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/sicetr/59/7/59_342/_pdf
Reference24 articles.
1. 1) L.S. Dalenogare, G.B. Benitez, N.F. Ayala and A.G. Frank: The expected contribution of industry 4.0 technologies for industrial performance, International Journal of Production Economics, 204, 383/394 (2018)
2. 2) A.G. Frank, L.S. Dalenogare and N.F. Ayala: Industry 4.0 technologies: Implementation patterns in manufacturing companies, International Journal of Production Economics, 210, 15/26 (2019)
3. 3) S. Vaidya, P. Ambad and S. Bhosle: Industry 4.0: a glimpse, Procedia Manufacturing, 20, 233/238 (2018)
4. 4) W. Wang, Y. Zhang, J. Gu and J. Wang: A proactive manufacturing resources assignment method based on production performance prediction for the smart factory, IEEE Transactions on Industrial Informatics, 1 (2021)
5. 5) G. Chakraborty, T. Kamiyama, H. Takahashi and T. Kinoshita: An efficient anomaly detection in quasi-periodic time series data: A case study with ECG, ITISE 2017: Time Series Analysis and Forecasting, 147/157 (2017)
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