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Intelligent edge processing

  • Ljiljana StojanovicEmail author
Conference paper
Part of the Technologien für die intelligente Automation book series (TIA, volume 11)

Abstract

Innovating maintenance is crucial for the competitiveness of the European manufacturing, pressured to increase flexibility and efficiency while reducing costs. Initiatives related to Industrie 4.0 have been showing the potential of using advanced/pervasive sensing, big data analytics and cloud-based services. In this paper, we present the edge part of our solution for self-healing manufacturing to early-predict equipment condition and make optimized recommendations for adjustments and maintenance to ensure normal operations. The intelligent edge is advanced, affordable and easily integrated, cyber-physical solution for predicting maintenance of machine tools in varying manufacturing environments, by using new connectivity, sensors and big data analytics methods. The proposed solution is capable to integrate information from many different sources, by including structured, semi-structured and unstructured data. The key innovation is in IoTization through dynamic, multi-modal, smart data gathering and integration based on the semantic technologies.

Keywords

Edge Computing Semantics Intelligent data fusion 

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References

  1. 1. Big Data in Manufacturing: BDA and IoT Can Optimize Production Lines and the Bottom Line— but Much of the Industry Isn’t There Yet, Frost & Sullivan, Big Data & Analytics, December 2016Google Scholar
  2. 2. Big Data, XaaS, and IoT Transforming Manufacturing Automation, Disruptive Technologies Transforming Traditional Processes to Enable Smart Manufacturing, July 2016Google Scholar
  3. 3. M. Schleipen, et al.: Requirements and concept for Plug&Work. Automatisierungstechnik 63:801-820, 2015Google Scholar
  4. 4. D. Riemer, et al, StreamPipes: solving the challenge with semantic stream pipelines. DEBS 2015: 330-331Google Scholar
  5. L. Stojanovic, et al., Big-data-driven anomaly detection in industry (4.0): An approach and a case study. BigData 2016: 1647-1652Google Scholar
  6. 6. L. Stojanovic, et al., PREMIuM: Big Data Platform for enabling Self-healing Manufacturing, ICE 2017Google Scholar
  7. 7. F: Ganz, Automated Semantic Knowledge Acquisition from Sensor Data; IEEE Systems Special Issue, 2016Google Scholar
  8. 8. R.Volz, , et al.,: Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web. J. Web Sem. 1(2): 187-206 (2004)Google Scholar
  9. 9. N Stojanovic, et al.,: Semantic Complex Event Reasoning - Beyond Complex Event Processing. Foundations for the Web of Information and Services 2011: 253-279Google Scholar
  10. 10. I. Grangel-González, et al.: Towards a Semantic Administrative Shell for Industry 4.0 Components, ICSC, Seite 230-237. IEEE Computer Society, (2016)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Fraunhofer IOSBKarlsruheGermany

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