An analytics environment architecture for industrial cyber-physical systems big data solutions

Eduardo A. Hinojosa-Palafox, Oscar M. Rodríguez-Elías*, José A. Hoyo-Montaño, Jesús H. Pacheco-Ramírez, José M. Nieto-Jalil

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

Original languageEnglish
Article number4282
JournalSensors (Switzerland)
Volume21
Issue number13
DOIs
StatePublished - 1 Jul 2021

Bibliographical note

Funding Information:
CONACYT has partially supported this work with scholarship No. 890778, and Tecnol?gico Nacional de M?xico under project grant 10980.21-P.

Funding Information:
Funding: CONACYT has partially supported this work with scholarship No. 890778, and Tecnológico Nacional de México under project grant 10980.21-P.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Analytics environment architecture
  • CPS analytics
  • Industrial Big Data analytics
  • Industrial Big Data architecture design

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