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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 Orcid Logo, Cinzia Giannetti Orcid Logo

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

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

  • Accepted Manuscript under embargo until: 12th November 2022
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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Keywords: Industry 4.0; Predictive maintenance; Knowledge-based system; Chronicle mining; Ontology reasoning
College: College of Science
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