Automated health condition diagnosis of in situ wood utility poles using an intelligent non-destructive evaluation (NDE) framework

Yu, Y; Subhani, M; Hoshyar, AN; Li, J; Li, H

Automated health condition diagnosis of in situ wood utility poles using an intelligent non-destructive evaluation (NDE) framework

Yu, Y

Subhani, M Hoshyar, AN Li, J

Li, H

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Publisher:: World Scientific Publishing
Publication Type:: Journal Article
Citation:: International Journal of Structural Stability and Dynamics, 2020, 20, (10)
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Subhani, M
dc.contributor.author	Hoshyar, AN
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048
dc.contributor.author	Li, H
dc.date.accessioned	2021-02-24T04:51:26Z
dc.date.available	2021-02-24T04:51:26Z
dc.date.issued	2020-01-01
dc.identifier.citation	International Journal of Structural Stability and Dynamics, 2020, 20, (10)
dc.identifier.issn	0219-4554
dc.identifier.issn	1793-6764
dc.identifier.uri	http://hdl.handle.net/10453/146389
dc.description.abstract	© 2020 World Scientific Publishing Company. Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney.
dc.language	English
dc.publisher	World Scientific Publishing
dc.relation	Ausgrid
dc.relation	http://purl.org/au-research/grants/arc/LP110200162
dc.relation.ispartof	International Journal of Structural Stability and Dynamics
dc.relation.isbasedon	10.1142/S021945542042002X
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0913 Mechanical Engineering
dc.title	Automated health condition diagnosis of in situ wood utility poles using an intelligent non-destructive evaluation (NDE) framework
dc.type	Journal Article
utslib.citation.volume	20
utslib.for	0905 Civil Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-02-24T04:51:24Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	10

Abstract:

© 2020 World Scientific Publishing Company. Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney.

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http://hdl.handle.net/10453/146389