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Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation

2017-09Journal article Restricted access
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dc.contributor.authorCabrera, Diego
dc.contributor.authorSancho, Fernando
dc.contributor.authorLi, Chuan
dc.contributor.authorCerrada, Mariela
dc.contributor.authorSanchez, Rene-Vinicio
dc.contributor.authorPacheco, Fannia
dc.contributor.authorValente de Oliveira, JOSÉ
dc.date.accessioned2019-11-20T15:07:25Z
dc.date.available2019-11-20T15:07:25Z
dc.date.issued2017-09
dc.description.abstractSignals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods. (C) 2017 Elsevier B.V. All rights reserved.
dc.description.sponsorshipR&D projects Ministeriode Economia yCompet-itividad of Cobierno de Espana [TH12012-37434, T1N2013-41086-P]
dc.description.sponsorshipEuropean FEDER funds
dc.description.sponsorshipGIDTEC [002-002-2016-03-03]
dc.description.sponsorshipUniversidad Politecnica Salesians sede Cuenca
dc.description.sponsorshipSecretariat for Higher Education, Science.Technology and Innovation (SENESCVT) of the Republic of Ecuador
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1016/j.asoc.2017.04.016
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.urihttp://hdl.handle.net/10400.1/13026
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.subjectGenetic Algorithm
dc.subjectVibration signal
dc.subjectDiagnosis
dc.subjectMachinery
dc.subjectTransform
dc.titleAutomatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage64
oaire.citation.startPage53
oaire.citation.titleApplied Soft Computing
oaire.citation.volume58
person.familyNameLUÍS VALENTE DE OLIVEIRA
person.givenNameJOSÉ
person.identifier.ciencia-id1F12-C1D3-7717
person.identifier.orcid0000-0001-5337-5699
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublicationbb726e73-690c-4a33-822e-c47bdac3035b
relation.isAuthorOfPublication.latestForDiscoverybb726e73-690c-4a33-822e-c47bdac3035b

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