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.
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