A New Implementation of Digital Twins for Fault Diagnosis of Large Industrial Equipment

Abstract

Refurbishment and remanufacturing play a vital role in the sustainability of the large industrial field, which aims at restoring the equipment that is close to the end of their life. The EU-funded project RECLAIM proposes new approaches and techniques to support these two activities in order to achieve saving valuable materials and resources by renewing and recycling the mechanical equipment rather than scraping them when they exceed the end of the lifetime. As the most critical part of predictive maintenance in RECLAIM, the fault diagnosis technique could provide the necessary information about the identification of the failure type, thus making suitable maintenance strategies. In this paper, we propose a novel implementation method that can combine the digital twins with the fault diagnosis of large industrial equipment. Experiment result and analysis demonstrate that the proposed framework performs well for the fault diagnosis of rolling bearing.

Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
Additional Information: Copyright: © 2021 All copyrights are reserved by Yuchun Xu, published by Coalesce Research Group. This work is licensed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Funding: European Union’s Horizon 2020 research and innovation programme under grant agreement No 869884.
Uncontrolled Keywords: Digital Twins,Fault Diagnosis,Predictive Maintenance,Rolling Bearing
Full Text Link:
Related URLs: https://crgjour ... trial-equipment (Publisher URL)
PURE Output Type: Article
Published Date: 2021-06-15
Published Online Date: 2021-06-15
Accepted Date: 2021-06-01
Authors: Zhang, Ming (ORCID Profile 0000-0001-5202-5574)
Amaitik, Nasser (ORCID Profile 0000-0002-0962-4341)
Xu, Yuchun (ORCID Profile 0000-0001-6388-813X)
Rossini, Rosaria
Bosi, Ilaria
Cedola, Ariel Pablo

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