Moore, J. orcid.org/0000-0002-5182-9439 and Sawyer, D. (2024) Equipment health monitoring for industrial robotic arms. In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE International Conference on Automation Science and Engineering (CASE), 28 Aug - 01 Sep 2024, Bari, Italy. Institute of Electrical and Electronics Engineers (IEEE) ISBN 979-8-3503-5852-0
Abstract
The topic of equipment health monitoring (EHM) for robotics, including condition monitoring (CM), process monitoring (PM) and predictive maintenance (PdM) is of great interest within the literature, however, there is a significant lack of historical datasets for techniques to be tested upon. Commercial offerings in this area are often manufacturer specific, meaning that fleets that include robots from multiple suppliers cannot easily have performance/condition compared across the fleet. To address this, the work presented within this paper includes an accelerated wear test (AWT) conducted on an industrial robotic arm whilst being monitored using a suite of retrofitted sensors. The resulting data from the AWT is then analysed through a variety of techniques, including regression models, classification models, and a long short-term memory (LSTM) autoencoder, to demonstrate the potential for such methods to be utilised for robot EHM. Additionally, the associated dataset captured during the AWT is to be made openly available through the University of Sheffield’s online research data repository, ORDA, to allow further research to be conducted.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2024 The Author(s). Except as otherwise noted, this author-accepted version of a proceedings paper published in 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Adaptation models; Accuracy; Service robots; Predictive models; Robot sensing systems; Manipulators; Data models; Maintenance; Robots; Long short term memory |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > University of Sheffield Research Centres and Institutes > AMRC with Boeing (Sheffield) The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > AMRC with Boeing (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Jun 2024 09:54 |
Last Modified: | 28 Oct 2024 09:42 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/CASE59546.2024.10711629 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213611 |
Download
Filename: Equipment Health Monitoring for Industrial Robotic Arms.pdf
Licence: CC-BY 4.0