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dc.contributor.authorKannan, Vigneshwar
dc.contributor.authorLi, Huaizhong
dc.contributor.authorDzung, Viet Dao
dc.date.accessioned2019-12-03T01:52:34Z
dc.date.available2019-12-03T01:52:34Z
dc.date.issued2019
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/access.2019.2954704
dc.identifier.urihttp://hdl.handle.net/10072/389464
dc.description.abstractEnvelope analysis is a commonly used technique in fault diagnosis of rolling element bearings. The selection of a suitable frequency band for demodulation in envelope analysis has traditionally relied on the expertise of diagnosis technicians. The manual selection does not always give the best possible results in revealing the defect frequencies. To overcome this problem, a new demodulation band optimization approach is proposed which is based on a real-coded genetic algorithm with a novel fitness function and crossover selection process. The fitness function uses the ratio between fault frequency peaks and the maximum peak not corresponding to defects in the envelope spectrum. The crossover selection process uses the triangle series method to divide the probability of individuals in the population based on the fitness score obtained. The proposed method is assessed using vibration signals from two different rotor-bearing systems, i.e., a bearing testrig with seeded defects and the Case Western Reserve University bearing dataset. For all the cases, the method can find the optimized demodulation bands successfully for bearing fault detection. The method is further benchmarked with a well-established fast kurtogram approach which proves the effectiveness and superior capability of the developed algorithm, though the computational complexity needs improvement in future work.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofpagefrom168828
dc.relation.ispartofpageto168838
dc.relation.ispartofjournalIEEE Access
dc.relation.ispartofvolume7
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchTechnology
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode10
dc.titleDemodulation Band Optimization in Envelope Analysis for Fault Diagnosis of Rolling Element Bearings Using a Real-Coded Genetic Algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationKannan, V; Li, H; Dao, DV, Demodulation Band Optimization in Envelope Analysis for Fault Diagnosis of Rolling Element Bearings Using a Real-Coded Genetic Algorithm, IEEE Access, 2019, 7, pp. 168828-168838
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2019-11-29T05:04:20Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorLi, Huaizhong
gro.griffith.authorDao, Dzung V.
gro.griffith.authorKannan, Vigneshwar


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