KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0

Cao, Qiushi; Zanni-Merk, Cecilia; Samet, Ahmed; Reich, Christoph; Bertrand, François de; Beckmann, Arnold; Giannetti, Cinzia

doi:10.1016/j.rcim.2021.102281

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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

Robotics and Computer-Integrated Manufacturing, Volume: 74, Start page: 102281

Swansea University Authors: Qiushi Cao, Arnold Beckmann , Cinzia Giannetti

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DOI (Published version): 10.1016/j.rcim.2021.102281

Published in:	Robotics and Computer-Integrated Manufacturing
ISSN:	0736-5845
Published:	Elsevier BV 2022
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa58508
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first_indexed	2021-10-28T15:19:52Z
last_indexed	2023-01-11T14:39:09Z
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spelling	2022-10-31T13:46:35.9726570 v2 58508 2021-10-28 KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0 5c00afca4cb5fa62e43bda660a1a27b5 Qiushi Cao Qiushi Cao true false 1439ebd690110a50a797b7ec78cca600 0000-0001-7958-5790 Arnold Beckmann Arnold Beckmann true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2021-10-28 MTLS Journal Article Robotics and Computer-Integrated Manufacturing 74 102281 Elsevier BV 0736-5845 Industry 4.0; Predictive maintenance; Knowledge-based system; Chronicle mining; Ontology reasoning 1 4 2022 2022-04-01 10.1016/j.rcim.2021.102281 COLLEGE NANME Materials Science and Engineering COLLEGE CODE MTLS Swansea University 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]. 2022-10-31T13:46:35.9726570 2021-10-28T16:10:27.1366034 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Qiushi Cao 1 Cecilia Zanni-Merk 2 Ahmed Samet 3 Christoph Reich 4 François de Bertrand de Beuvron 5 Arnold Beckmann 0000-0001-7958-5790 6 Cinzia Giannetti 0000-0003-0339-5872 7 58508__21357__61ded58d9c9a436093485e16e9f1a239.pdf Main.pdf 2021-10-28T16:21:00.3152565 Output 7206137 application/pdf Accepted Manuscript true 2022-11-12T00:00:00.0000000 ©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) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title	KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
spellingShingle	KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0 Qiushi Cao Arnold Beckmann Cinzia Giannetti
title_short	KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
title_full	KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
title_fullStr	KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
title_full_unstemmed	KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
title_sort	KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
author_id_str_mv	5c00afca4cb5fa62e43bda660a1a27b5 1439ebd690110a50a797b7ec78cca600 a8d947a38cb58a8d2dfe6f50cb7eb1c6
author_id_fullname_str_mv	5c00afca4cb5fa62e43bda660a1a27b5_*_Qiushi Cao 1439ebd690110a50a797b7ec78cca600__Arnold Beckmann a8d947a38cb58a8d2dfe6f50cb7eb1c6_**_Cinzia Giannetti
author	Qiushi Cao Arnold Beckmann Cinzia Giannetti
author2	Qiushi Cao Cecilia Zanni-Merk Ahmed Samet Christoph Reich François de Bertrand de Beuvron Arnold Beckmann Cinzia Giannetti
format	Journal article
container_title	Robotics and Computer-Integrated Manufacturing
container_volume	74
container_start_page	102281
publishDate	2022
institution	Swansea University
issn	0736-5845
doi_str_mv	10.1016/j.rcim.2021.102281
publisher	Elsevier BV
college_str	Faculty of Science and Engineering
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KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0

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