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A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine
dc.creator | Solís Martín, David | es |
dc.creator | Galán Páez, Juan | es |
dc.creator | Borrego Díaz, Joaquín | es |
dc.date.accessioned | 2022-06-28T10:50:20Z | |
dc.date.available | 2022-06-28T10:50:20Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Solís Martín, D., Galán Páez, J. y Borrego Díaz, J. (2021). A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine. En PHM 2021: Annual Conference of the PHM Society Virtual Conference: PHM Society. | |
dc.identifier.issn | 2325-0178 | es |
dc.identifier.uri | https://hdl.handle.net/11441/134749 | |
dc.description.abstract | his paper presents the data-driven techniques and method ologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Con volutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to extract a low-dimensional feature vector using the normalized raw data as input. The second DCNN ingests a list of vectors taken from the former DCNN and estimates the RUL. Model selection was carried out by means of Bayesian optimization using a repeated random subsampling validation approach. The proposed methodol ogy was ranked in the third place of the 2021 PHM Confer ence Data Challenge. | es |
dc.description.sponsorship | Agencia Estatal de Investigación PID2019-109152GB-I00/AEI/10.13039/501100011033 | es |
dc.format | application/pdf | es |
dc.format.extent | 7 | es |
dc.language.iso | eng | es |
dc.publisher | PHM Society | es |
dc.relation.ispartof | PHM 2021: Annual Conference of the PHM Society (2021). | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | 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.projectID | PID2019-109152GB-I00/AEI/10.13039/501100011033 | es |
dc.relation.publisherversion | https://papers.phmsociety.org/index.php/phmconf/article/view/3110 | es |
dc.identifier.doi | 10.36001/phmconf.2021.v13i1.3110 | es |
dc.contributor.group | Universidad de Sevilla. TIC-137: Lógica, Computación e Ingeniería del Conocimiento | es |
dc.eventtitle | PHM 2021: Annual Conference of the PHM Society | es |
dc.eventinstitution | Virtual Conference | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
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