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DC Field | Value | Language |
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dc.contributor.author | Cabrera, Diego | - |
dc.contributor.author | Peña Ortega, Mario Patricio | - |
dc.contributor.author | Sánchez, René Vinicio | - |
dc.contributor.author | Cerrada, Mariela | - |
dc.date.accessioned | 2021-01-21T03:41:22Z | - |
dc.date.available | 2021-01-21T03:41:22Z | - |
dc.date.issued | 2020 | - |
dc.identifier.isbn | 978-172819365-6 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | http://dspace.ucuenca.edu.ec/handle/123456789/35477 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098552392&doi=10.1109%2fANDESCON50619.2020.9272146&partnerID=40&md5=03bb41d18cc4b836817e4a200ebde94c | - |
dc.description.abstract | The Prognostics and Health Management (PHM) approach aims to reduce potential failures or machine downtime by determining the system state through the identification of the signals changes produced by the system's faults. Machine learning (ML) approaches for fault diagnosis usually have high-dimensional feature space that can be obtained from signal processing. Nevertheless, as more features are included in the ML algorithms the processing time increases, there is a tendency for overfitting, and the performance may even decrease. Feature selection has multiple goals including building more simple and comprehensible models, improving the performance on ML algorithms, and preparing clean and understandable data. This paper proposes a methodological framework based on a cluster validity index (CVI) and Sequential Forward Search (SFS) to select the best subset of features applied on the problem of fault severity classification in rolling bearing. The results show that a perfect classification can be obtained with KNN with at least six selected features. | - |
dc.language.iso | es_ES | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.source | 2020 IEEE ANDESCON, ANDESCON 2020 | - |
dc.subject | Classification | - |
dc.subject | Fault detection | - |
dc.subject | Cluster validity index | - |
dc.subject | Feature selection | - |
dc.subject | Bearings | - |
dc.title | Fast feature selection based on cluster validity index applied on data-driven bearing fault detection | - |
dc.type | ARTÍCULO DE CONFERENCIA | - |
dc.description.city | Quito | - |
dc.ucuenca.idautor | sgrp-4075-002 | - |
dc.ucuenca.idautor | sgrp-4075-004 | - |
dc.ucuenca.idautor | sgrp-4075-003 | - |
dc.ucuenca.idautor | 0302168141 | - |
dc.identifier.doi | 10.1109/ANDESCON50619.2020.9272146 | - |
dc.ucuenca.embargoend | 2050-12-31 | - |
dc.ucuenca.version | Versión publicada | - |
dc.ucuenca.embargointerno | 2050-12-31 | - |
dc.ucuenca.areaconocimientounescoamplio | 07 - Ingeniería, Industria y Construcción | - |
dc.ucuenca.afiliacion | Sánchez, R., Universidad Politecnica Salesiana, Cuenca, Ecuador | - |
dc.ucuenca.afiliacion | Cerrada, M., Universidad Politecnica Salesiana, Cuenca, Ecuador | - |
dc.ucuenca.afiliacion | Cabrera, D., Universidad Politecnica Salesiana, Cuenca, Ecuador | - |
dc.ucuenca.afiliacion | Peña, M., Universidad de Cuenca, Cuenca, Ecuador | - |
dc.ucuenca.volumen | 13 October 2020 | - |
dc.ucuenca.indicebibliografico | SCOPUS | - |
dc.ucuenca.numerocitaciones | 0 | - |
dc.ucuenca.areaconocimientofrascatiamplio | 2. Ingeniería y Tecnología | - |
dc.ucuenca.pais | ECUADOR | - |
dc.ucuenca.conferencia | 2020 IEEE ANDESCON, ANDESCON 2020 | - |
dc.ucuenca.areaconocimientofrascatiespecifico | 2.2 Ingenierias Eléctrica, Electrónica e Información | - |
dc.ucuenca.areaconocimientofrascatidetallado | 2.2.3 Sistemas de Automatización y Control | - |
dc.ucuenca.areaconocimientounescoespecifico | 071 - Ingeniería y Profesiones Afines | - |
dc.ucuenca.areaconocimientounescodetallado | 0714 - Electrónica y Automatización | - |
dc.ucuenca.fechainicioconferencia | 2020-10-13 | - |
dc.ucuenca.fechafinconferencia | 2020-10-16 | - |
dc.ucuenca.organizadorconferencia | Mara Falconí, President of IEEE Ecuador; Carlos Lozano, Universidad de los Andes, Colombia. | - |
dc.ucuenca.comiteorganizadorconferencia | Germán Arévalo, Universidad Politécnica Salesiana, Ecuador dc.ucuenca.urifuente | - |
dc.ucuenca.urifuente | https://ieeexplore.ieee.org/xpl/conhome/9271969/proceeding | - |
Appears in Collections: | Artículos |
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documento.pdf Until 2050-12-31 | document | 217.87 kB | Adobe PDF | View/Open Request a copy |
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