Acoustical damage detection of wind turbine blade using the improved incremental support vector data description

Chen, B; Yu, S; Yu, Y; Zhou, Y

Acoustical damage detection of wind turbine blade using the improved incremental support vector data description

Chen, B Yu, S Yu, Y

Zhou, Y

Permalink

Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Renewable Energy, 2020, 156, pp. 548-557
Issue Date:: 2020-08-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0960148120306285-main.pdf	Published version	2.83 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chen, B
dc.contributor.author	Yu, S
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Zhou, Y
dc.date.accessioned	2020-10-27T19:47:52Z
dc.date.available	2020-10-27T19:47:52Z
dc.date.issued	2020-08-01
dc.identifier.citation	Renewable Energy, 2020, 156, pp. 548-557
dc.identifier.issn	0960-1481
dc.identifier.issn	1879-0682
dc.identifier.uri	http://hdl.handle.net/10453/143542
dc.description.abstract	© 2020 Elsevier Ltd The blade is a crucial part of wind turbine for generating electricity and prone to damage due to harsh external environment. Accurate damage detection of wind turbine blade (WTB) is still a prominent challenge. This paper presents an acoustical detection method for damage identification of the WTB based on pattern recognition. In the proposed method, sound pulse extraction of the WTB is first investigated through physical method in combination with the filter and sliding window. Subsequently, the wavelet packet energy ratios of acoustic signal are introduced to characterize the discrepancy between intact and cracked sound pulses, and the support vector data description (SVDD) model is built for WTB damage detection. Besides, an improved incremental learning method is presented and employed to adaptively update the SVDD model, which aims at simplifying calculation procedure. Finally, the performance of proposed method is evaluated using experimental data collected from the WTBs with both intact and damaged conditions in commercial wind farms. It is demonstrated that proposed method has improvement in prediction accuracy compared to previous incremental SVDD models and performs the best on training time.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Renewable Energy
dc.relation.isbasedon	10.1016/j.renene.2020.04.096
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Energy
dc.title	Acoustical damage detection of wind turbine blade using the improved incremental support vector data description
dc.type	Journal Article
utslib.citation.volume	156
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
utslib.for	0915 Interdisciplinary Engineering
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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-27T19:47:43Z
pubs.publication-status	Published
pubs.volume	156

Abstract:

© 2020 Elsevier Ltd The blade is a crucial part of wind turbine for generating electricity and prone to damage due to harsh external environment. Accurate damage detection of wind turbine blade (WTB) is still a prominent challenge. This paper presents an acoustical detection method for damage identification of the WTB based on pattern recognition. In the proposed method, sound pulse extraction of the WTB is first investigated through physical method in combination with the filter and sliding window. Subsequently, the wavelet packet energy ratios of acoustic signal are introduced to characterize the discrepancy between intact and cracked sound pulses, and the support vector data description (SVDD) model is built for WTB damage detection. Besides, an improved incremental learning method is presented and employed to adaptively update the SVDD model, which aims at simplifying calculation procedure. Finally, the performance of proposed method is evaluated using experimental data collected from the WTBs with both intact and damaged conditions in commercial wind farms. It is demonstrated that proposed method has improvement in prediction accuracy compared to previous incremental SVDD models and performs the best on training time.

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

http://hdl.handle.net/10453/143542