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.
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.
| Original language | English |
|---|---|
| Number of pages | 7 |
| Journal | Journal of Robotics and Mechanical Engineering Research |
| Volume | 1 |
| Issue number | 1 |
| Publication status | Published - 15 Jun 2021 |
Bibliographical note
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 reproductionin 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.
Keywords
- Digital Twins
- Fault Diagnosis
- Predictive Maintenance
- Rolling Bearing