Behavioral Modeling of Real Dynamic Processes in an Industry 4.0-Oriented Context

Dylan Molinié, Kurosh Madani, Véronique Amarger

2023

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 of 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.

Paper Citation

in Harvard Style

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 - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 510-517. DOI: 10.5220/0012134500003541

in Bibtex Style

@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 - Volume 1: DATA},
year={2023},
pages={510-517},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012134500003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Behavioral Modeling of Real Dynamic Processes in an Industry 4.0-Oriented Context
SN - 978-989-758-664-4
AU - Molinié D.
AU - Madani K.
AU - Amarger V.
PY - 2023
SP - 510
EP - 517
DO - 10.5220/0012134500003541
PB - SciTePress