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Authors: Dylan Molinié ; Kurosh Madani and Véronique Amarger

Affiliation: LISSI Laboratory EA 3956, Université Paris-Est Créteil, Sénart-FB Institute of Technology, Campus de Sénart, 36-37 Rue Georges Charpak, F-77567 Lieusaint, France

Keyword(s): Industry 4.0, Machine Learning, Unsupervised Clustering, Multi-Modeling, Temporal Prediction.

Abstract: With the Industry 4.0, new fashions to think the industry emerge: the production units are now orchestrated from some decentralized places to collaborate to improve efficiency, save time and resources, and reduce costs. To that end, Artificial Intelligence is expected to help manage units, prevent disruptions, predict failures, etc. A way to do so may consist in modeling the temporal evolution of the processes to track, predict and prevent the future failures; such modeling can be performed using the full dataset at once, but it may be more accurate to isolate the regions of the feature space where there is little variation in the data, then model these local regions separately, and finally connect all of them to build the final model of the system. This paper proposes to identify the compact regions of the feature space with unsupervised clustering, and then to model them with data-driven regression. The proposed methodology is tested on real industrial data, obtained in the scope o f an Industry 4.0-oriented European project, and its accuracy is compared to that achieved by a global model; results show that local modeling achieves better accuracy, both during learning and testing stages. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Molinié, D.; Madani, K. and Amarger, V. (2023). Behavioral Modeling of Real Dynamic Processes in an Industry 4.0-Oriented Context. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 510-517. DOI: 10.5220/0012134500003541

@conference{data23,
author={Dylan Molinié. and Kurosh Madani. and Véronique Amarger.},
title={Behavioral Modeling of Real Dynamic Processes in an Industry 4.0-Oriented Context},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={510-517},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012134500003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - Behavioral Modeling of Real Dynamic Processes in an Industry 4.0-Oriented Context
SN - 978-989-758-664-4
IS - 2184-285X
AU - Molinié, D.
AU - Madani, K.
AU - Amarger, V.
PY - 2023
SP - 510
EP - 517
DO - 10.5220/0012134500003541
PB - SciTePress