Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model

Yuwono, M; Qin, Y; Zhou, J; Guo, Y; Celler, BG; Su, SW

Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model

Yuwono, M

Qin, Y Zhou, J Guo, Y Celler, BG Su, SW

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Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2016, 47 pp. 88 - 100
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Yuwono, M https://orcid.org/0000-0002-0824-8233	en_US
dc.contributor.author	Qin, Y	en_US
dc.contributor.author	Zhou, J	en_US
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Celler, BG	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2016, 47 pp. 88 - 100	en_US
dc.identifier.issn	0952-1976	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122539
dc.description.abstract	© 2015 Elsevier Ltd. Ball bearings are integral elements in most rotating manufacturing machineries. While detecting defective bearing is relatively straightforward, discovering the source of defect requires advanced signal processing techniques. This paper proposes an automatic bearing defect diagnosis method based on Swarm Rapid Centroid Estimation (SRCE) and Hidden Markov Model (HMM). Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Liftering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98.02%, 96.03%, and 2.65%, respectively, on the bearing fault vibration data provided by Case School of Engineering of the Case Western Reserve University (CSE) which warrants further investigation.	en_US
dc.relation.ispartof	Engineering Applications of Artificial Intelligence	en_US
dc.relation.isbasedon	10.1016/j.engappai.2015.03.007	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model	en_US
dc.type	Journal Article
utslib.citation.volume	47	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	47	en_US

Abstract:

© 2015 Elsevier Ltd. Ball bearings are integral elements in most rotating manufacturing machineries. While detecting defective bearing is relatively straightforward, discovering the source of defect requires advanced signal processing techniques. This paper proposes an automatic bearing defect diagnosis method based on Swarm Rapid Centroid Estimation (SRCE) and Hidden Markov Model (HMM). Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Liftering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98.02%, 96.03%, and 2.65%, respectively, on the bearing fault vibration data provided by Case School of Engineering of the Case Western Reserve University (CSE) which warrants further investigation.

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

http://hdl.handle.net/10453/122539