Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction

Chen, B; Peng, F; Wang, H; Yu, Y

Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction

Chen, B Peng, F Wang, H Yu, Y

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Mechanism and Machine Theory, 2020, 154, pp. 104051-104051
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Chen, B
dc.contributor.author	Peng, F
dc.contributor.author	Wang, H
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.date.accessioned	2020-11-13T22:00:02Z
dc.date.available	2020-11-13T22:00:02Z
dc.date.issued	2020-12-01
dc.identifier.citation	Mechanism and Machine Theory, 2020, 154, pp. 104051-104051
dc.identifier.issn	0094-114X
dc.identifier.uri	http://hdl.handle.net/10453/143986
dc.description.abstract	© 2020 The high frequency resonance (HFR) technique is regarded as a powerful tool for fault diagnosis of rolling element bearings. Different from the usage of the HFR in single fault, the determination of multiple resonant frequency bands under the compound faults and extraneous random impulses is still a challenging task. This paper develops a novel compound fault identification method based on adaptive resonant frequency band extraction. The improved redundant second generation wavelet packet transform is first presented to decompose vibration signal into various narrow bands for providing a fine separation of fault signatures. Then the squared envelope spectrum sparsity criteria is designed to quantify fault characteristics buried in narrow frequency bands. Consequently, the squared envelope spectrum sparsogram is constructed to highlight optimal resonant bands, and the compound faults can be well detected by band-pass filtering and envelope analysis. The numerical and experimental results confirm effectiveness and superiority of the proposed method, which is more sensitive to fault-related impulses and robust to extraneous interferences.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Mechanism and Machine Theory
dc.relation.isbasedon	10.1016/j.mechmachtheory.2020.104051
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0910 Manufacturing Engineering, 0913 Mechanical Engineering
dc.subject.classification	Design Practice & Management
dc.title	Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction
dc.type	Journal Article
utslib.citation.volume	154
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0910 Manufacturing 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/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	2020-11-13T21:59:55Z
pubs.publication-status	Accepted
pubs.volume	154

Abstract:

© 2020 The high frequency resonance (HFR) technique is regarded as a powerful tool for fault diagnosis of rolling element bearings. Different from the usage of the HFR in single fault, the determination of multiple resonant frequency bands under the compound faults and extraneous random impulses is still a challenging task. This paper develops a novel compound fault identification method based on adaptive resonant frequency band extraction. The improved redundant second generation wavelet packet transform is first presented to decompose vibration signal into various narrow bands for providing a fine separation of fault signatures. Then the squared envelope spectrum sparsity criteria is designed to quantify fault characteristics buried in narrow frequency bands. Consequently, the squared envelope spectrum sparsogram is constructed to highlight optimal resonant bands, and the compound faults can be well detected by band-pass filtering and envelope analysis. The numerical and experimental results confirm effectiveness and superiority of the proposed method, which is more sensitive to fault-related impulses and robust to extraneous interferences.

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http://hdl.handle.net/10453/143986