loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: David Kerkkamp 1 ; Zaharah A. Bukhsh 2 ; Yingqian Zhang 2 and Nils Jansen 1

Affiliations: 1 Radboud University, Nijmegen, The Netherlands ; 2 Eindhoven University of Technology, Eindhoven, The Netherlands

Keyword(s): Maintenance Planning, Deep Reinforcement Learning, Graph Neural Networks, Sewer Asset Management.

Abstract: Reinforcement learning (RL) has shown promising performance in several applications such as robotics and games. However, the use of RL in emerging real-world domains such as smart industry and asset management remains scarce. This paper addresses the problem of optimal maintenance planning using historical data. We propose a novel Deep RL (DRL) framework based on Graph Convolutional Networks (GCN) to leverage the inherent graph structure of typical assets. As demonstrator, we employ an underground sewer pipe network. In particular, instead of dispersed maintenance actions of individual pipes across the network, the GCN ensures the grouping of maintenance actions of geographically close pipes. We perform experiments using the distinct physical characteristics, deterioration profiles, and historical data of sewer inspections within an urban environment. The results show that combining Deep Q-Networks (DQN) with GCN leads to structurally more reliable networks and a higher degree of ma intenance grouping, compared to DQN with fully-connected layers and standard preventive and corrective maintenance strategy that are often adopted in practice. Our approach shows potential for developing efficient and practical maintenance plans in terms of cost and reliability. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 74.48.170.251

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kerkkamp, D.; Bukhsh, Z.; Zhang, Y. and Jansen, N. (2022). Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 574-585. DOI: 10.5220/0010907500003116

@conference{icaart22,
author={David Kerkkamp. and Zaharah A. Bukhsh. and Yingqian Zhang. and Nils Jansen.},
title={Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={574-585},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010907500003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks
SN - 978-989-758-547-0
IS - 2184-433X
AU - Kerkkamp, D.
AU - Bukhsh, Z.
AU - Zhang, Y.
AU - Jansen, N.
PY - 2022
SP - 574
EP - 585
DO - 10.5220/0010907500003116
PB - SciTePress