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Smart Maintenance in Asset Management – Application with Deep Learning

  • Harald RødsethEmail author
  • Ragnhild J. Eleftheriadis
  • Zhe Li
  • Jingyue Li
Conference paper
  • 191 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 634)

Abstract

With the onset the digitalization and Industry 4.0, the maintenance function and asset management in a company is forming towards Smart Maintenance. An essential application in smart maintenance is to improve the maintenance planning function with better criticality assessment. With the aid from artificial intelligence it is considered that maintenance planning will provide better and faster decision making in maintenance management. The aim of this article is to develop smart maintenance planning based on principles both from asset management and machine learning. The result demonstrates a use case of criticality assessment for maintenance planning and comprise computation of anomaly degree (AD) as well as calculation of profit loss indicator (PLI). The risk matrix in the criticality assessment is then constructed by both AD and PLI and will then aid the maintenance planner in better and faster decision making. It is concluded that more industrial use cases should be conducted representing different industry branches.

Keywords

Smart maintenance Anomaly detection Asset management 

Notes

Acknowledgements

The authors wish to thank for valuable input from both the research project CPS-plant (grant number: 267750), as well as the research project CIRCit – Circular Economy Integration in the Nordic Industry for Enhanced Sustainability and Competitiveness (grant number: 83144).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Harald Rødseth
    • 1
    Email author
  • Ragnhild J. Eleftheriadis
    • 2
  • Zhe Li
    • 3
  • Jingyue Li
    • 3
  1. 1.Department of Mechanical and Industrial EngineeringNorwegian University of Science and Technology (NTNU)TrondheimNorway
  2. 2.Product and Production DevelopmentSINTEF Manufacturing ASVestre TotenNorway
  3. 3.Department of Computer ScienceNorwegian University of Science and Technology (NTNU)TrondheimNorway

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