Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search

Sun, L; Xin, Y; Chen, T; Feng, B

Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search

Sun, L

Xin, Y Chen, T Feng, B

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ELECTRONICS, 2023, 12, (2)
Issue Date:: 2023-01

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Field	Value	Language
dc.contributor.author	Sun, L https://orcid.org/0000-0002-9830-1527
dc.contributor.author	Xin, Y
dc.contributor.author	Chen, T
dc.contributor.author	Feng, B
dc.date.accessioned	2024-04-11T03:27:39Z
dc.date.available	2024-04-11T03:27:39Z
dc.date.issued	2023-01
dc.identifier.citation	ELECTRONICS, 2023, 12, (2)
dc.identifier.issn	1450-5843
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/177713
dc.description.abstract	<jats:p>In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology to extract fault features. Second, a clustering hybrid initialization technique is given for feature selection, combining the Louvain algorithm and the feature number. Third, a mutation strategy based on Levy flight is proposed, which effectively utilizes high-quality information to guide subsequent searches. In addition, a dynamic abandonment probability is proposed based on population sorting, which can effectively retain high-quality solutions and accelerate the convergence of the algorithm. Experimental results from nine UCI datasets show the effectiveness of the proposed improvement strategy. The open-source bearing dataset is used to compare the fault diagnosis accuracy of different algorithms. The experimental results show that the diagnostic error rate of this method is only 1.13%, which significantly improves classification accuracy and effectively realizes feature dimension reduction in fault datasets. Compared to similar methods, the proposed method has better comprehensive performance.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ELECTRONICS
dc.relation.isbasedon	10.3390/electronics12020459
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0906 Electrical and Electronic Engineering
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 Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-11T03:27:35Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	2

Abstract:

In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology to extract fault features. Second, a clustering hybrid initialization technique is given for feature selection, combining the Louvain algorithm and the feature number. Third, a mutation strategy based on Levy flight is proposed, which effectively utilizes high-quality information to guide subsequent searches. In addition, a dynamic abandonment probability is proposed based on population sorting, which can effectively retain high-quality solutions and accelerate the convergence of the algorithm. Experimental results from nine UCI datasets show the effectiveness of the proposed improvement strategy. The open-source bearing dataset is used to compare the fault diagnosis accuracy of different algorithms. The experimental results show that the diagnostic error rate of this method is only 1.13%, which significantly improves classification accuracy and effectively realizes feature dimension reduction in fault datasets. Compared to similar methods, the proposed method has better comprehensive performance.

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http://hdl.handle.net/10453/177713