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VGAN: generalizing MSE GAN and WGAN-GP for robot fault diagnosis

2022Journal article Restricted
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dc.contributor.authorPu, Ziqiang
dc.contributor.authorCabrera, Diego
dc.contributor.authorLi, Chuan
dc.contributor.authorValente de Oliveira, JOSÉ
dc.date.accessioned2022-12-07T14:37:01Z
dc.date.available2022-12-07T14:37:01Z
dc.date.issued2022
dc.description.abstractGenerative adversarial networks (GANs) have shown their potential for data generation. However, this type of generative model often suffers from oscillating training processes and mode collapse, among other issues. To mitigate these, this work proposes a generalization of both mean square error (mse) GAN and Wasserstein GAN (WGAN) with gradient penalty, referred to as VGAN. Within the framework of conditional WGAN with gradient penalty, VGAN resorts to the Vapnik V-matrix-based criterion that generalizes mse. Also, a novel early stopping-like strategy is proposed that keeps track during training of the most suitable model. A comprehensive set of experiments on a fault-diagnosis task for an industrial robot where the generative model is used as a data augmentation tool for dealing with imbalance datasets is presented. The statistical analysis of the results shows that the proposed model outperforms nine other models, including vanilla GAN, conditional WGAN with and without conventional regularization, and synthetic minority oversampling technique, a classic data augmentation technique.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/MIS.2022.3168356pt_PT
dc.identifier.issn1541-1672
dc.identifier.urihttp://hdl.handle.net/10400.1/18600
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.subjectGenerative adversarial networkspt_PT
dc.subjectGeneratorspt_PT
dc.subjectFault diagnosisIntelligent systemspt_PT
dc.subjectRobotsData modelspt_PT
dc.subjectData generationpt_PT
dc.subjectTrainingpt_PT
dc.subjectconditional Wasserstein generative adversarial networkregularizationV-matrixfault diagnosisroboticspt_PT
dc.titleVGAN: generalizing MSE GAN and WGAN-GP for robot fault diagnosispt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.citation.endPage75pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage65pt_PT
oaire.citation.titleIEEE Intelligent Systemspt_PT
oaire.citation.volume37pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePu
person.familyNameLUÍS VALENTE DE OLIVEIRA
person.givenNameZiqiang
person.givenNameJOSÉ
person.identifier.ciencia-id1F12-C1D3-7717
person.identifier.orcid0000-0003-4410-3493
person.identifier.orcid0000-0001-5337-5699
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication48f95fa9-9a9c-457d-9517-6dca8f4b0264
relation.isAuthorOfPublicationbb726e73-690c-4a33-822e-c47bdac3035b
relation.isAuthorOfPublication.latestForDiscoverybb726e73-690c-4a33-822e-c47bdac3035b
relation.isProjectOfPublication9df77b70-8231-47e7-9b34-c702e9c6021c
relation.isProjectOfPublication.latestForDiscovery9df77b70-8231-47e7-9b34-c702e9c6021c

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