Journal article 848 views 397 downloads
KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
Qiushi Cao,
Cecilia Zanni-Merk,
Ahmed Samet,
Christoph Reich,
François de Bertrand de Beuvron,
Arnold Beckmann 
,
Cinzia Giannetti
,
Cinzia Giannetti
Robotics and Computer-Integrated Manufacturing, Volume: 74, Start page: 102281
Swansea University Authors:
Qiushi Cao, Arnold Beckmann , Cinzia Giannetti
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PDF | Accepted Manuscript
©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND)
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DOI (Published version): 10.1016/j.rcim.2021.102281
Abstract
KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
| Published in: | Robotics and Computer-Integrated Manufacturing |
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| ISSN: | 0736-5845 |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58508 |
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| Keywords: |
Industry 4.0; Predictive maintenance; Knowledge-based system; Chronicle mining; Ontology reasoning |
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| College: |
Faculty of Science and Engineering |
| Funders: |
his work is mainly funded by INTERREG Upper Rhine (European Regional Development Fund), Germany and the Ministries for Research of Baden-Württemberg, Rheinland-Pfalz (Germany) and the Grand Est French Region in the framework of the Science Offensive Upper Rhine HALFBACK project. Q. Cao and A. Beckmann (in part) are also supported by the Engineering and Physical Sciences Research Council (EPSRC), United Kingdom [grant number EPSRC EP/S018107/1]. C. Giannetti’s work is supported by the EPSRC, United Kingdom project [EP/S001387/1]. |
| Start Page: |
102281 |