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Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories

  • Gökan May
  • Nikos Kyriakoulis
  • Konstantinos Apostolou
  • Sangje Cho
  • Konstantinos Grevenitis
  • Stefanos Kokkorikos
  • Jovana Milenkovic
  • Dimitris Kiritsis
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 536)

Abstract

Process output and profitability of the operations are mainly determined by how the equipment is being used. The production planning, operations and machine maintenance influence the overall equipment effectiveness (OEE) of the machinery, resulting in more ‘good parts’ at the end of the day. The target of the predictive maintenance approaches in this respect is to increase efficiency and effectiveness by optimizing the way machines are being used and to decrease the costs of unplanned interventions for the customer. To this end, development of ad-hoc strategies and their seamless integration into predictive maintenance systems is envisaged to bring substantial advantages in terms of productivity and competitiveness enhancement for manufacturing systems, representing a leap towards the real implementation of the Industry 4.0 vision. Inspired by this challenge, the study provides an approach to develop a novel predictive maintenance platform capable of preventing unexpected-breakdowns based on integrated strategies for extending the operating life span of production systems. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled Z-Bre4k, i.e. Strategies and predictive maintenance models wrapped around physical systems for zero-unexpected-breakdowns and increased operating life of factories.

Keywords

Industry 4.0 Predictive maintenance Big data Asset management Smart factories Sustainable manufacturing Industrial production 

Notes

Acknowledgements

This work has been carried out in the framework of Z-Bre4k Project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 768869.

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Gökan May
    • 1
  • Nikos Kyriakoulis
    • 2
  • Konstantinos Apostolou
    • 3
  • Sangje Cho
    • 1
  • Konstantinos Grevenitis
    • 3
  • Stefanos Kokkorikos
    • 2
  • Jovana Milenkovic
    • 3
  • Dimitris Kiritsis
    • 1
  1. 1.EPFL, ICT for Sustainable Manufacturing, EPFL SCI-STI-DKLausanneSwitzerland
  2. 2.Core Innovation and Technology O.EAthensGreece
  3. 3.ATLANTIS Engineering S.A.ThessalonikiGreece

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