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Artículo
One-shot fault diagnosis of 3D printers through improved feature space learning
dc.creator | Li, Chuan | es |
dc.creator | Cabrera, Diego | es |
dc.creator | Sancho Caparrini, Fernando | es |
dc.creator | Sánchez, René-Vinicio | es |
dc.creator | Cerrada, Mariela | es |
dc.creator | Oliveira, José Valente de | es |
dc.date.accessioned | 2021-04-19T07:35:44Z | |
dc.date.available | 2021-04-19T07:35:44Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Li, C., Cabrera, D., Sancho Caparrini, F., Sánchez, R., Cerrada, M. y Oliveira, J.V.d. (2020). One-shot fault diagnosis of 3D printers through improved feature space learning. IEEE Transactions on Industrial Electronics | |
dc.identifier.issn | 0278-0046 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107272 | |
dc.description.abstract | Signal acquisition from mechanical systems working in faulty conditions is normally expensive. As a consequence, supervised learning-based approaches are hardly applicable. To address this problem, a one-shot learning-based approach is proposed for multi-class classification of signals coming from a feature space created only from healthy condition signals and one single sample for each faulty class. First, a transformation mapping between the input signal space and a feature space is learned through a bidirectional generative adversarial network. Next, the identification of different health condition regions in this feature space is carried out by means of a single input signal per fault. The method is applied to three fault diagnosis problems of a 3D printer and outperforms other methods in the literature. | es |
dc.format | application/pdf | es |
dc.format.extent | 9 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Transactions on Industrial Electronics | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | Fault diagnosis | es |
dc.subject | One-shot learning | es |
dc.subject | 3D printer | es |
dc.title | One-shot fault diagnosis of 3D printers through improved feature space learning | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9161402 | es |
dc.identifier.doi | 10.1109/TIE.2020.3013546 | es |
dc.journaltitle | IEEE Transactions on Industrial Electronics | es |
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