Cloud-based Industrial Cyber-Physical System for Data-driven Reasoning. A Review and Use Case on an Industry 4.0 Pilot Line

Villalonga Jaén, Alberto and Beruvides López, Gerardo and Castaño Romero, Fernando and Haber Guerra, Rodolfo E. (2020). Cloud-based Industrial Cyber-Physical System for Data-driven Reasoning. A Review and Use Case on an Industry 4.0 Pilot Line. "IEEE Transactions on Industrial Informatics", v. 16 (n. 9); pp. 5975-5984. https://doi.org/10.1109/TII.2020.2971057.

Description

Title: Cloud-based Industrial Cyber-Physical System for Data-driven Reasoning. A Review and Use Case on an Industry 4.0 Pilot Line
Author/s:
  • Villalonga Jaén, Alberto
  • Beruvides López, Gerardo
  • Castaño Romero, Fernando
  • Haber Guerra, Rodolfo E.
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Industrial Informatics
Date: September 2020
Volume: 16
Subjects:
Freetext Keywords: Cloud-to-edge-based concept, reinforcement learning, Industrial Cyber-Physical Systems, Machine-learning library, Condition-based monitoring, smart manufacturing, Industry 4.0.
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - Non commercial

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Abstract

Nowadays, reconfiguration and adaptation by means of optimal re-parametrization in industrial cyber-physical systems (ICPS) is one of the bottlenecks for the digital transformation of the manufacturing industry. This work proposes a cloud-to-edges-based ICPS equipped with machine learning techniques. The proposed reasoning module includes a learning procedure based on two reinforcement learning techniques, running in parallel, for updating both the data-conditioning and processing strategy and the prediction model. The presented solution distributes computational resources and analytic engines in multiple layers and independent modules increasing the smartness and the autonomy for monitoring and control the behavior at shop floor level. The suitability of the proposed solution, evaluated in a pilot line, is endorsed by fast time response (i.e., 0.01s at the edge level) and the appropriate setting of optimal operational parameters for guaranteeing the desired quality surface roughness during macro and micro milling operations.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2017-86915-C3-3-RCOGDRIVEUnspecifiedTécnicas de Inteligencia Artificial y Ayuda a la Navegación Autónoma

More information

Item ID: 64947
DC Identifier: http://oa.upm.es/64947/
OAI Identifier: oai:oa.upm.es:64947
DOI: 10.1109/TII.2020.2971057
Official URL: https://ieeexplore.ieee.org/document/8978483
Deposited by: Dr. Rodolfo Haber
Deposited on: 23 Oct 2020 09:08
Last Modified: 23 Oct 2020 09:27
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