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DC Field | Value | Language |
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dc.contributor.author | Razgon, M | - |
dc.contributor.author | Mousavi, A | - |
dc.date.accessioned | 2020-08-30T23:37:23Z | - |
dc.date.available | 2020-08-30T23:37:23Z | - |
dc.date.issued | 2020-09-03 | - |
dc.identifier | 219 | - |
dc.identifier | ORCID iD: Alireza Mousavi https://orcid.org/0000-0003-0360-2712 | - |
dc.identifier.citation | Razgon, M. and Mousavi, A. (2020) ‘Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept’, Algorithms, 13 (9), 219, pp. 1-22. doi: 10.3390/a13090219. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/21495 | - |
dc.description | Correction published on 12 March 2021, see Algorithms 2021, 14(3), 86. doi: 10.3390/a14030086. | - |
dc.description.abstract | Copyright © 2020 by the authors. In this paper we propose a novel approach of rule learning called Relaxed Separate-and- Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology that does not require elimination of covered rows. This method can be seen as a generalization of the methods of SeCo and weighted covering that does not suffer from fragmentation. We present an empirical investigation of the proposed RSC approach in the area of Predictive Maintenance (PdM) of complex manufacturing machines, to predict forthcoming failures of these machines. In particular, we use for experiments a real industrial case study of a machine which manufactures the plastic bottle. We compare the RSC approach with a Decision Tree (DT) based and SeCo algorithms and demonstrate that RSC significantly outperforms both DT based and SeCo rule learners. We conclude that the proposed RSC approach is promising for PdM guided by rule learning. | - |
dc.description.sponsorship | European Union’s Horizon 2020 research and innovation program | en_US |
dc.format.extent | 1 - 22 (22) | - |
dc.format.medium | Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | predictive maintenance | en_US |
dc.subject | failure prediction | en_US |
dc.subject | rule learning | en_US |
dc.subject | decision tree | en_US |
dc.subject | machine learning | en_US |
dc.title | Relaxed Rule-based Learning for Automated Predictive Maintenance: proof of concept | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/a13090219 | - |
dc.relation.isPartOf | Algorithms | - |
pubs.issue | 9 | - |
pubs.publication-status | Published | - |
pubs.volume | 13 | - |
dc.identifier.eissn | 1999-4893 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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