Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning

Using artificial intelligence for maintenance planning is useful for many industries to have a smart decision-making tool that delivers the best maintenance policy to minimize the expected maintenance costs. In this paper, a deep reinforcement learning method is used to provide a new dynamic mainten...

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Bibliographic Details
Published in:	Quality engineering Vol. 34; no. 1; pp. 16 - 35
Main Authors:	Yousefi, Nooshin, Tsianikas, Stamatis, Coit, David W.
Format:	Journal Article
Language:	English
Published:	Milwaukee Taylor & Francis 02.01.2022 Taylor & Francis Ltd
Subjects:	Algorithms Artificial intelligence Decision making Deep learning Deep Q network Deep reinforcement learning Degradation Discretization dynamic maintenance gamma process Machine learning Maintenance costs Markov decision process Markov processes Neural networks Pattern recognition Teaching methods
ISSN:	0898-2112, 1532-4222
Online Access:	Get full text
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