dc.creator | Li, Chuan | es |
dc.creator | Cabrera, Diego | es |
dc.creator | Sancho Caparrini, Fernando | es |
dc.creator | Cerrada, Mariela | es |
dc.creator | Sánchez, René-Vinicio | es |
dc.creator | Estupinan, Edgar | es |
dc.date.accessioned | 2021-04-16T07:56:05Z | |
dc.date.available | 2021-04-16T07:56:05Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Li, C., Cabrera, D., Sancho Caparrini, F., Cerrada, M., Sánchez, R. y Estupinan, E. (2021). From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine. ISA Transactions, 110 (April 2021), 357-367. | |
dc.identifier.issn | 0019-0578 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107186 | |
dc.description.abstract | The lack of faulty condition data reduces the feasibility of supervised learning for fault detection or
fault severity discrimination in new manufacturing technologies. To deal with this issue, one-class
learning arises for building binary discriminative models using only healthy condition data. However,
these models have not been extrapolated to severity discrimination. This paper proposes to extend
OCSVM, which is typically used for fault detection, to 3D printer fault severity discrimination. First, a
set of features is extracted from a set of normal signals. An optimized OCSVM model is obtained by
tuning the kernel and model hyperparameters. The resulting models are evaluated for fault detection
and fault severity discrimination using a proposed performance evaluation approach. Experimental
comparisons for belt-based faults in 3D printers show that the distance to the hyperplane has the
information to discriminate the severity level, and its use is feasible. The proposed hyperparameter
optimization technique improves the OCSVM for fault detection and severity discrimination compared
to some other methods. | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | ISA Transactions, 110 (April 2021), 357-367. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | fault Detection | es |
dc.subject | Severity discrimination | es |
dc.subject | One-class support vector machine | es |
dc.subject | 3D printer | es |
dc.subject | Bidirectional generative adversarial network | es |
dc.title | From fault detection to one-class severity discrimination of 3D printers with one-class support vector machine | 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://www.sciencedirect.com/science/article/pii/S0019057820304390 | es |
dc.identifier.doi | 10.1016/j.isatra.2020.10.036 | es |
dc.journaltitle | ISA Transactions | es |
dc.publication.volumen | 110 | es |
dc.publication.issue | April 2021 | es |
dc.publication.initialPage | 357 | es |
dc.publication.endPage | 367 | es |