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A Hybrid Cloud-to-Edge Predictive Maintenance Platform

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Predictive Maintenance in Smart Factories

Part of the book series: Information Fusion and Data Science ((IFDS))

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Abstract

The role of maintenance in the industry has been shown to improve companies’ productivity and profitability. Industry 4.0 revolutionised this field by exploiting emergent cloud technologies and IoT to enable predictive maintenance. Significant benefits can be obtained by taking advantage of historical data and Industrial IoT streams, combined with high and distributed computing power. Many approaches have been proposed for predictive maintenance solutions in the industry. Typically, the processing and storage of enormous amounts of data can be effectively performed cloud-side (e.g., training complex predictive models), minimising infrastructure costs and maintenance. On the other hand, raw data collected on the shop floor can be successfully processed locally at the edge, without necessarily being transferred to the cloud. In this way, peripheral computational resources are exploited, and network loads are reduced. This work aims to investigate these approaches and integrate the advantages of each solution into a novel flexible ecosystem. As a result, a new unified solution, named SERENA Cloud Platform. The result addresses many challenges of the current state-of-the-art architectures for predictive maintenance, from hybrid cloud-to-edge solutions to intermodal collaboration, heterogeneous data management, services orchestration, and security.

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Acknowledgements

This research has been partially funded by the European project SERENA VerSatilE plug-and-play platform enabling REmote predictive mainteNAnce (Grant Agreement: 767561).

Author information

Authors and Affiliations

  1. Research & Innovation, Engineering Ingegneria Informatica S.p.A., Palermo, Italy

    Angelo Marguglio, Giuseppe Veneziano & Pietro Greco

  2. Fraunhofer Institute for Production Technology IPT and Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany

    Sven Jung, Robert Siegburg & Robert H. Schmitt

  3. Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy

    Simone Monaco, Daniele Apiletti & Tania Cerquitelli

  4. Laboratory for Manufacturing Systems and Automation, University of Patras, Patras, Greece

    Nikolaos Nikolakis

  5. Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy

    Enrico Macii

Authors
  1. Angelo Marguglio
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  2. Giuseppe Veneziano
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  3. Pietro Greco
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  4. Sven Jung
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  5. Robert Siegburg
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  6. Robert H. Schmitt
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  7. Simone Monaco
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  8. Daniele Apiletti
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  9. Nikolaos Nikolakis
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  10. Tania Cerquitelli
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  11. Enrico Macii
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Corresponding author

Correspondence to Tania Cerquitelli .

Editor information

Editors and Affiliations

  1. Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  2. Laboratory for Manufacturing Systems and Automation, University of Patras, Patras, Greece

    Nikolaos Nikolakis

  3. Dell EMC Research Europe, Cork, Ireland

    Niamh O’Mahony

  4. Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy

    Enrico Macii

  5. COMAU S.p.A., Grugliasco, Torino, Italy

    Massimo Ippolito

  6. Laboratory for Manufacturing Systems and Automation, University of Patras, Patras, Greece

    Sotirios Makris

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Marguglio, A. et al. (2021). A Hybrid Cloud-to-Edge Predictive Maintenance Platform. In: Cerquitelli, T., Nikolakis, N., O’Mahony, N., Macii, E., Ippolito, M., Makris, S. (eds) Predictive Maintenance in Smart Factories. Information Fusion and Data Science. Springer, Singapore. https://doi.org/10.1007/978-981-16-2940-2_2

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