An acoustic fault sample expansion method based on noise disturbance

Wei, N; Zhang, L; Qiu, X

An acoustic fault sample expansion method based on noise disturbance

Wei, N Zhang, L Qiu, X

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Publication Type:: Conference Proceeding
Citation:: 24th International Congress on Sound and Vibration, ICSV 2017, 2017
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Wei, N	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Qiu, X https://orcid.org/0000-0002-5181-1220	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	24th International Congress on Sound and Vibration, ICSV 2017, 2017	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117903
dc.description.abstract	Sample expansion is an effective approach to resolve the incomplete sample problem in ship acoustics fault source identification. A novel sample expansion method based on noise disturbance is proposed. Firstly, the expanded samples are generated by adding different signal-tonoise ratio noise to the real acoustics fault samples. Then, the training collection is enhanced by combining the original training samples with the expanded samples. Experimental results on both simulated data and measured data demonstrate that the classifier performance can be improved by the expanded training sample set.	en_US
dc.relation.ispartof	24th International Congress on Sound and Vibration, ICSV 2017	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	An acoustic fault sample expansion method based on noise disturbance	en_US
dc.type	Conference Proceeding
utslib.for	0203 Classical Physics	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
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

Sample expansion is an effective approach to resolve the incomplete sample problem in ship acoustics fault source identification. A novel sample expansion method based on noise disturbance is proposed. Firstly, the expanded samples are generated by adding different signal-tonoise ratio noise to the real acoustics fault samples. Then, the training collection is enhanced by combining the original training samples with the expanded samples. Experimental results on both simulated data and measured data demonstrate that the classifier performance can be improved by the expanded training sample set.

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http://hdl.handle.net/10453/117903