Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications

Description

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. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020. / Mechanical and Aeronautical Engineering / MEng (Mechanical Engineering) / Unrestricted

Links & Downloads

1. http://hdl.handle.net/2263/78533
2. *
3. A2021

Tags

UCTD

Maintenance Policy Optimisation

Deep Reinforcement Learning

Multi-agent Reinforcement Learning

Additional Fields

Identifer	oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/78533
Date	January 2020
Creators	Ludeke, Ricardo Pedro João
Contributors	Heyns, P.S. (Philippus Stephanus), [e-mail]
Publisher	University of Pretoria
Source Sets	South African National ETD Portal
Language	English
Detected Language	English
Type	Dissertation
Rights	© 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.