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Reinforcement Learning for Scheduling of Maintenance

  • Michael Knowles
  • David Baglee
  • Stefan Wermter
Conference paper

Abstract

Improving maintenance scheduling has become an area of crucial importance in recent years. Condition-based maintenance (CBM) has started to move away from scheduled maintenance by providing an indication of the likelihood of failure. Improving the timing of maintenance based on this information to maintain high reliability without resorting to over-maintenance remains, however, a problem. In this paper we propose Reinforcement Learning (RL), to improve long term reward for a multistage decision based on feedback given either during or at the end of a sequence of actions, as a potential solution to this problem. Several indicative scenarios are presented and simulated experiments illustrate the performance of RL in this application.

Keywords

Reinforcement Learning Optimal Schedule Digital Object Identifier Reliability Function Maintenance Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Michael Knowles
    • 1
  • David Baglee
    • 1
  • Stefan Wermter
    • 2
  1. 1.Institute for Automotive and Manufacturing Advanced Practice (AMAP), University of SunderlandSunderlandUK
  2. 2.Knowledge Technology Group, Department of InformaticsUniversity of HamburgHamburgGermany

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