Semiautomatic Generation of Reference Values for Identifying Obstructions in Continuous Casting
Abstract
Clogging of submerged entry valves in the continuous casting process increase the frequency of interruptions in operation. These interruptions increase operating costs, and can cause a variety of quality problems. The absence of data sets labeled for clogging has prevented the application of machine learning methods for predicting this anomaly. This work sought to develop semiautomatic techniques for labeling reference data sets. As a first step, a clustering technique was applied over time series using the DBSCAN algorithm. The generated clusters were used as seeds for a semi-supervised label propagation process. This process generated a database that was validated by specialists and 100% of the data labeled as obstructions were considered correctly labeled.
Keywords:
Continuous Casting, Semiautomatic techniques for labeling, clustering
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S. García, J. Luengo, and F. Herrera. Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowledge-Based Systems, 98:1–29, apr 2016.
L. McInnes, J. Healy, and J. Melville. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018.
L.Ometto,S.Challapalli,M.Polo,G.Cestari,A.Villagrossi,M.Sandri,andE.Pellegrini. Successful Use Case Applications of Artificial Intelligence in the Steel Industry. In AISTech2019 Proceedings of the Iron and Steel Technology Conference, pages 2573–2584. AIST, 2019.
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S. Rodpongpun, V. Niennattrakul, and A. Ratanamahatana. Selective Subsequence Time Series clustering. Knowledge-Based Systems, 35:361–368, 2012.
P. J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53 – 65, 1987.
B. G. Thomas and H. Bai. Tundish Nozzle Clogging-Application Of Computational Mo- dels. In 18rd Process Technology Division Conference Proceedings, volume 18. Iron and Steel Society, 2001.
M. Vannucci and V. Colla. Novel classification method for sensitive problems and uneven datasets based on neural networks and fuzzy logic. Applied Soft Computing Journal, 11(2):2383–2390, 2011.
M. Vannucci, V. Colla, G. Nastasi, and N. Matarese. Detection of rare events within in- dustrial datasets by means of data resampling and specific algorithms. International Journal of Simulation: Systems, Science and Technology, 11(3):1–11, 2010.
B. Wang, Z. Tu, and J. Tsotsos. Dynamic label propagation for semi-supervised multi- class multi-label classification. Proceedings of the IEEE International Conference on Computer Vision, 52:425–432, 12 2013.
F. Yuan, X. Wang, J. Zhang, and L. Zhang. Online forecasting model of tundish nozzle clogging. Journal of University of Science and Technology Beijing: Mineral Metal- lurgy Materials (Eng Ed), 13(1):21–24, feb 2006.
X. Zhu and Z. Ghahramani. Learning from labeled and unlabeled data with label propa- gation. Technical Report CMU-CALD-02-107, Carnegie Mellon University, Pitts- burgh, PA, 2002.
Published
2020-06-30
How to Cite
RAMOS, Leandro Rodrigues; KOMATI, Karin Satie; BOLDT, Francisco de Assis; ANDRADE, Jefferson Oliveira.
Semiautomatic Generation of Reference Values for Identifying Obstructions in Continuous Casting. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 47. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 116-127.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2020.11322.
