A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots

To, AWK; Paul, G; Liu, D

A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots

To, AWK Paul, G

Liu, D

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Publication Type:: Journal Article
Citation:: Robotics and Computer-Integrated Manufacturing, 2018, 49 pp. 13 - 23
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	To, AWK	en_US
dc.contributor.author	Paul, G https://orcid.org/0000-0002-3478-0020	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.date.available	2020-05-25T19:05:46Z
dc.date.issued	2018-02-01	en_US
dc.identifier.citation	Robotics and Computer-Integrated Manufacturing, 2018, 49 pp. 13 - 23	en_US
dc.identifier.issn	0736-5845	en_US
dc.identifier.uri	http://hdl.handle.net/10453/79023
dc.description.abstract	© 2017 Elsevier Ltd This paper presents a comprehensive approach to diagnose for faults that may occur during a robotic grit-blasting operation. The approach proposes the use of information collected from multiple sensors (RGB-D camera, audio and pressure transducers) to detect for 1) the real-time position of the grit-blasting spot and 2) the real-time state within the blasting line (i.e. compressed air only). The outcome of this approach will enable a grit-blasting robot to autonomous diagnose for faults and take corrective actions during the blasting operation. Experiments are conducted in a laboratory and in a grit-blasting chamber during real grit-blasting to demonstrate the proposed approach. Accuracy of 95% and above has been achieved in the experiments.	en_US
dc.relation.ispartof	Robotics and Computer-Integrated Manufacturing	en_US
dc.relation.isbasedon	10.1016/j.rcim.2017.05.008	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots	en_US
dc.type	Journal Article
utslib.citation.volume	49	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

© 2017 Elsevier Ltd This paper presents a comprehensive approach to diagnose for faults that may occur during a robotic grit-blasting operation. The approach proposes the use of information collected from multiple sensors (RGB-D camera, audio and pressure transducers) to detect for 1) the real-time position of the grit-blasting spot and 2) the real-time state within the blasting line (i.e. compressed air only). The outcome of this approach will enable a grit-blasting robot to autonomous diagnose for faults and take corrective actions during the blasting operation. Experiments are conducted in a laboratory and in a grit-blasting chamber during real grit-blasting to demonstrate the proposed approach. Accuracy of 95% and above has been achieved in the experiments.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/79023