Abstract
Machine Learning (ML) based data analytics provide methods to cope with the massive data amounts, generated by the various manufacturing processes. In this respect, the maintenance is among the most critical activities concerned by the industrial data analytics in the contexts of industry 4.0. We believe that the maintenance activities can be managed by the predictive processes dwelling on ML methods with the help of meta-learning based data analytics solutions. The challenge is then to facilitate the industry 4.0 actors, who are supposedly not AI specialists, with the application of machine learning. The automated machine learning seems to be the area dealing with this challenge. In this paper, we first show the problematic of assisting industry 4.0 actors to implement ML algorithms in the context of predictive maintenance. We then present a novel AutoML based framework. It aims to enable industry 4.0 actors and researchers, who presumably have limited competencies in machine learning, to generate ML-based data analytics solutions and their deployment in the manufacturing workflows. The framework implements primarily the approaches based on the meta-learning for this purpose. In the context of Industry 4.0 such approaches lead to the implementation of the smart factory concepts. It makes the factory processes more proactive on the basis of predictive knowledge extracted from the various manufacturing devices, sensors, and business processes.
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This work has been supported, in part, by Hestim, CNRST Morocco, and University of the Littoral Cote d’Opale, Calais France.
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Garouani, M., Hamlich, M., Ahmad, A., Bouneffa, M., Bourguin, G., Lewandowski, A. (2022). Toward an Automatic Assistance Framework for the Selection and Configuration of Machine Learning Based Data Analytics Solutions in Industry 4.0. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_1
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