Yeardley, A.S. orcid.org/0000-0001-7996-0589, Ejeh, J.O. orcid.org/0000-0003-2542-1496, Allen, L. orcid.org/0000-0001-7669-3534 et al. (2 more authors) (2022) Integrating machine learning techniques into optimal maintenance scheduling. Computers & Chemical Engineering, 166. 107958. ISSN: 0098-1354
Abstract
Poor maintenance regimes often contribute to unplanned downtimes, quality defects and accidents; thus it is crucial to apply an effective maintenance strategy to achieve efficient and safe processes. Industry 4.0 has brought about a proliferation of digital data and with it new opportunities to advance and improve the way maintenance activities are planned. Here, we propose a novel methodology that utilises machine learning to predict both machine faults and repair time, and uses this data to underpin the scheduling of maintenance activities. This can be used to plan maintenance, and optimise the schedule with a cost objective within the constraints of labour availability and plant layout. When applied to a dataset obtained using a simulated Fischertechnik (FT) model, this methodology reduced the overall plant maintenance costs by decreasing unplanned downtimes and increasing maintenance efficiency. This work provides a promising first step towards improving the way maintenance tasks are approached in Industry 4.0.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Predictive maintenance; Optimisation; Maintenance scheduling; Machine learning; Time estimation model |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 884418 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Aug 2025 10:53 |
Last Modified: | 29 Aug 2025 10:53 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.compchemeng.2022.107958 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:230754 |