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Machine Learning for Cyber Physical Systems pp 87–96Cite as

A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance

A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance

  • Klaudia Kovacs5,
  • Fazel Ansari5,
  • Claudio Geisert7,
  • Eckart Uhlmann7,
  • Robert Glawar6 &
  • …
  • Wilfried Sihn5 
  • Conference paper
  • Open Access
  • First Online: 18 December 2018
  • 9322 Accesses

  • 5 Citations

Part of the Technologien für die intelligente Automation book series (TIA,volume 9)

Abstract

Digital transformation and evolution of integrated computational and visualisation technologies lead to new opportunities for reinforcing knowledge-based maintenance through collection, processing and provision of actionable information and recommendations for maintenance operators. Providing actionable information regarding both corrective and preventive maintenance activities at the right time may lead to reduce human failure and improve overall efficiency within maintenance processes. Selecting appropriate digital assistance systems (DAS), however, highly depends on hardware and IT infrastructure, software and interfaces as well as information provision methods such as visualization. The selection procedures can be challenging due to the wide range of services and products available on the market. In particular, underlying machine learning algorithms deployed by each product could provide certain level of intelligence and ultimately could transform diagnostic maintenance capabilities into predictive and prescriptive maintenance. This paper proposes a process-based model to facilitate the selection of suitable DAS for supporting maintenance operations in manufacturing industries. This solution is employed for a structured requirement elicitation from various application domains and ultimately mapping the requirements to existing digital assistance solutions. Using the proposed approach, a (combination of) digital assistance system is selected and linked to maintenance activities. For this purpose, we gain benefit from an in-house process modeling tool utilized for identifying and relating sequence of maintenance activities. Finally, we collect feedback through employing the selected digital assistance system to improve the quality of recommendations and to identify the strengths and weaknesses of each system in association to practical usecases from TU Wien Pilot-Factory Industry 4.0.

Keywords

  • Maintenance
  • Digital Assistance Systems
  • Process Model
  • Industry 4.0

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Author information

Authors and Affiliations

  1. Institute of Management Science, Vienna University of Technology (TU Wien), Vienna, Austria

    Klaudia Kovacs, Fazel Ansari & Wilfried Sihn

  2. Division of Production & Logistics Management, Fraunhofer Austria, Vienna, Austria

    Robert Glawar

  3. Fraunhofer Institute for Productions Systems and Design Technology IPK, Berlin, Germany

    Claudio Geisert & Eckart Uhlmann

Authors
  1. Klaudia Kovacs
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  2. Fazel Ansari
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  3. Claudio Geisert
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  4. Eckart Uhlmann
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  5. Robert Glawar
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  6. Wilfried Sihn
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Corresponding author

Correspondence to Klaudia Kovacs .

Editor information

Editors and Affiliations

  1. Institut für Optronik, Systemtechnik und Bildauswertung, Fraunhofer, Karlsruhe, Germany

    Prof. Dr. Jürgen Beyerer

  2. MRD, Fraunhofer Institute for Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, Germany

    Dr. Christian Kühnert

  3. inIT - Institut für industrielle Informationstechnik, Hochschule Ostwestfalen-Lippe, Lemgo, Germany

    Prof. Dr. Oliver Niggemann

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Kovacs, K., Ansari, F., Geisert, C., Uhlmann, E., Glawar, R., Sihn, W. (2019). A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_10

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  • DOI: https://doi.org/10.1007/978-3-662-58485-9_10

  • Published: 18 December 2018

  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-58484-2

  • Online ISBN: 978-3-662-58485-9

  • eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)

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