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Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications
Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613482566811648 |
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| access_status_str | Open Access |
| author2 | Heyns, P.S. (Philippus Stephanus) |
| author_browse | Heyns, P.S. (Philippus Stephanus) |
| author_facet | Heyns, P.S. (Philippus Stephanus) |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78533 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:51.203Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/78533 Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications Heyns, P.S. (Philippus Stephanus) rpjludeke@gmail.com Ludeke, Ricardo Pedro João UCTD Maintenance Policy Optimisation Deep Reinforcement Learning Multi-agent Reinforcement Learning Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020. Industrial operational environments are stochastic and can have complex system dynamics which introduce multiple levels of uncertainty. This uncertainty leads to sub-optimal decision making and resource allocation. Digitalisation and automation of production equipment and the maintenance environment enable predictive maintenance, meaning that equipment can be stopped for maintenance at the optimal time. Resource constraints in maintenance capacity could however result in further undesired downtime if maintenance cannot be performed when scheduled. In this dissertation the applicability of using a Multi-Agent Deep Reinforcement Learning based approach for decision making is investigated to determine the optimal maintenance scheduling policy in a fleet of assets where there are maintenance resource constraints. By considering the underlying system dynamics of maintenance capacity, as well as the health state of individual assets, a near-optimal decision making policy is found that increases equipment availability while also maximising maintenance capacity. The implemented solution is compared to a run-to-failure corrective maintenance strategy, a constant interval preventive maintenance strategy and a condition based predictive maintenance strategy. The proposed approach outperformed traditional maintenance strategies across several asset and operational maintenance performance metrics. It is concluded that Deep Reinforcement Learning based decision making for asset health management and resource allocation is more effective than human based decision making. mi2025 Mechanical and Aeronautical Engineering MEng (Mechanical Engineering) Unrestricted SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production SDG-11: Sustainable cities and communities 2021-02-12T10:06:02Z 2021-02-12T10:06:02Z 2021 2020 Dissertation * A2021 http://hdl.handle.net/2263/78533 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Maintenance Policy Optimisation Deep Reinforcement Learning Multi-agent Reinforcement Learning Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications |
| title | Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications |
| title_full | Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications |
| title_fullStr | Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications |
| title_full_unstemmed | Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications |
| title_short | Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications |
| title_sort | towards a deep reinforcement learning based approach for real time decision making and resource allocation for prognostics and health management applications |
| topic | UCTD Maintenance Policy Optimisation Deep Reinforcement Learning Multi-agent Reinforcement Learning Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities |
| url | http://hdl.handle.net/2263/78533 |