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2021 | OriginalPaper | Chapter

Research on Preprocessing Methods for Time Series Classification Using Machine Learning Models in the Domain of Radial-Axial Ring Rolling

Authors : S. Fahle, A. Kneißler, T. Glaser, B. Kuhlenkötter

Published in: Production at the leading edge of technology

Publisher: Springer Berlin Heidelberg

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Abstract

Machine learning models trained to predict certain outcomes bear great potential in a variety of applications. This research takes a step to elevate the hot forming technology of radial-axial ring rolling towards a fully digitalized and even more efficient forming technology. For successful machine learning the preprocessing step is essential. This paper presents current research regarding the most promising preprocessing approaches of time series data for the specific use case of classifying form errors of the radial-axial ring rolling process. By predicting form errors (in-situ), scrap and rework rates can be lowered due to an alert by the model for form errors in advance of a potential error, thus contributing to a more efficient industry. The data used exists in form of time series from log-data of an industrial used, single ring rolling machine. Concluding, the proposed preprocessing approaches are evaluated by comparing different model performances, trained on actual production data.

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Fahle, S., Prinz, C., Kuhlenkötter, B.: Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application. Procedia CIRP (2020, in press)

Fahle, S., Kuhlenkötter, B.: A framework for data integration and analysis in radial-axial ring rolling. In: 1st Conference on Production Systems and Logistics (2020)

Tan, C.W., Petitjean, F., Keogh, E., Webb, G.I.: Time series classification for varying length series. Preprint available at: https://arxiv.org/abs/1910.04341, arXiv:1910.04341v1, 1-23. (2019)

Kim, D., Lee, T., Kim, S., Lee, B., Youn, H.Y.: Adaptive packet scheduling in IoT environment based on Q-learning. Procedia Comput. Sci. 141, 247–254 (2018)CrossRef

Lubosch, M., Kunath, M., Winkler, H.: Industrial scheduling with Monte Carlo tree search and machine learning. Procedia CIRP 72, 1283–1287 (2018)CrossRef

Lavrik, E., Panasenko, I., Schmidt, H.R.: Advanced methods for the optical quality assurance of silicon sensors. Nucl. Instrum. Methods Phys. Res. Sect. Accel. Spect. Detect. Assoc. Equip. 922, 336–344 (2019)CrossRef

Ma, L., Xie, W., Zhang, Y.: Blister defect detection based on convolutional neural network for polymer lithium-ion battery. Appl. Sci. 9(6), 1085 (2019)CrossRef

Du Preez, A., Oosthuizen, G.A.: Machine learning in cutting processes as enabler for smart sustainable manufacturing. Procedia Manuf. 33, 810–817 (2019)CrossRef

Hwang, S., Jeon, G., Jeong, J., Lee, J.: A novel time series based Seq2Seq model for temperature prediction in firing furnace process. Procedia Comput. Sci. 155, 19–26 (2019)CrossRef

10.

Bagnall, A., Lines, J., Hills, J., Bostrom, A.: Time-series classification with COTE: the collective of transformation-based ensembles. IEEE Trans. Knowl. Data Eng. 27(9), 2522–2535 (2015)CrossRef

11.

Lines, J., Taylor, S., Bagnall, A.: Time series classification with HIVE-COTE. ACM Trans. Knowl. Discov. Data 12(5), 1–35 (2018)CrossRef

12.

Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2017)MathSciNetCrossRef

13.

Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.-C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Keogh, E.: The UCR time series archive. Available at: https://arxiv.org/abs/1810.07758 arXiv:1810.07758v2, 1–12 (2018)

14.

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 59(2), 195 (2019)MathSciNet

15.

Xing, Z., Pei, J., Keogh, E.: A brief survey on sequence classification. SIGKDD Explor. 12(1), 40–48 (2010)CrossRef

16.

He, G., Duan, Y., Peng, R., Jing, X., Qian, T., Wang, L.: Early classification on multivariate time series. Neurocomputing 149, 777–787 (2015)CrossRef

17.

Lin, Y.F., Chen, H.H., Tseng, V.S., Pei, J.: Reliable early classification on multivariate time series with numerical and categorical attributes. In: Cao, T., Lim, E.P., Zhou, Z.H., Ho, T.B., Cheung, D., Motoda, H. (eds.) Advances in Knowledge Discovery and Data Mining, vol. 9077, pp. 199–211. Springer, Cham (2015)CrossRef

18.

Mori, U., Mendiburu, A., Keogh, E., Lozano, J.A.: Reliable early classification of time series based on discriminating the classes over time. Data Min. Knowl. Disc. 31(1), 233–263 (2017)MathSciNetCrossRef

19.

Rußwurm, M., Lefèvre, S., Courty, N., Emonet, R., Körner, M., Tavenard, R.: End-to-end learning for early classification of time series. Preprint available at: https://arxiv.org/abs/1901.10681, arXiv:1901.10681v1, 1–9 (2019)

20.

Tavenard, R., Faouzi, J., Vandewiele, G., Divo, F., Androz, G., Holtz, C., Payne, Marie, Yurchak, Roman, Rußwurm, M., Kolar, K., Woods, E.: tslearn: a machine learning toolkit dedicated to time-series data. https://github.com/rtavenar/tslearn (2017)

21.

Thyssen: Technologiehandbuch: Einführung in die Ringwalztechnologie. Thyssen Wagner Maschinenbau GmbH, Dortmund (1990)

22.

Löning, M., Bagnall, A., Ganesh, S., Kazakov, V., Lines, J., Király, F.J.: sktime: A unified interface for machine learning with time series. Available at: https://arxiv.org/abs/1909.07872, arXiv:1909.07872v1, 1-9 (2019)

23.

Lucas, B., Shifaz, A., Pelletier, C., O’Neill, L., Zaidi, N., Goethals, B., Petitjean, F., Webb, G.I.: Proximity Forest: an effective and scalable distance-based classifier for time series. Data Min. Knowl. Disc. 33(3), 607–635 (2019)CrossRef

24.

Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Disc. 29(6), 1505–1530 (2015)MathSciNetMATHCrossRef

25.

Deng, H., Runger, G., Tuv, E., Vladimir, M.: A Time series forest for classification and feature extraction. Inf. Sci. 239, 142–153 (2013)MathSciNetMATHCrossRef

26.

Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Min. Knowl. Disc. 28(4), 851–881 (2014)MathSciNetMATHCrossRef

Title: Research on Preprocessing Methods for Time Series Classification Using Machine Learning Models in the Domain of Radial-Axial Ring Rolling
Authors: S. Fahle
A. Kneißler
T. Glaser
B. Kuhlenkötter
Publisher: Springer Berlin Heidelberg
Book: Production at the leading edge of technology
Print ISBN: 978-3-662-62137-0

Electronic ISBN: 978-3-662-62138-7

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-662-62138-7_49

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