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Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry
dc.contributor.author | Fernandes, Marta | |
dc.contributor.author | Canito, Alda | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.contributor.author | Conceição, Luís | |
dc.contributor.author | Praça, Isabel | |
dc.contributor.author | Marreiros, Goreti | |
dc.date.accessioned | 2024-07-01T11:58:10Z | |
dc.date.available | 2024-07-01T11:58:10Z | |
dc.date.issued | 2019-06 | |
dc.identifier.citation | M. Fernandes, A. Canito, V. Bolón-Canedo, L. Conceição, I. Praça, and G. Marreiros, "Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry", International Journal of Information Management, Vol. 46, Jun. 2019, pp. 252-262, doi: 10.1016/j.ijinfomgt.2018.10.006 | es_ES |
dc.identifier.issn | 0268-4012 | |
dc.identifier.uri | http://hdl.handle.net/2183/37589 | |
dc.description.abstract | [Abstract]: Proactive Maintenance practices are becoming more standard in industrial environments, with a direct and profound impact on the competitivity within the sector. These practices demand the continuous monitorization of industrial equipment, which generates extensive amounts of data. This information can be processed into useful knowledge with the use of machine learning algorithms. However, before the algorithms can effectively be applied, the data must go through an exploratory phase: assessing the meaning of the features and to which degree they are redundant. In this paper, we present the findings of the analysis conducted on a real-world dataset from a metallurgic company. A number of data analysis and feature selection methods are employed, uncovering several relationships, which are systematized in a rule-based model, and reducing the feature space from an initial 47-feature dataset to a 32-feature dataset. | es_ES |
dc.description.sponsorship | The present work has been developed under the EUREKA - ITEA2 Project INVALUE (ITEA-13015), INVALUE Project (ANI|P2020 17990), and has received funding from FEDER (ERDF) Funds through NORTE2020 program and from Portuguese National Funds through FCT (Portuguese Foundation for Science and Technology) under the project UID/EEA/00760/2013. These programs were not involved in the collection, analysis or interpretation of data nor had any role in the decision to submit the article for publication. | es_ES |
dc.description.sponsorship | Portugal. Fundação para a Ciência e a Tecnologia; UID/EEA/00760/2013 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier Ltd | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.ijinfomgt.2018.10.006 | es_ES |
dc.rights | Attribution-NonCommercial_NoDerivs 4.0 Internationsl (CC BY-NC-ND 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Predictive maintenance | es_ES |
dc.subject | Data analysis | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Rule-based model | es_ES |
dc.title | Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | International Journal of Information Management | es_ES |
UDC.volume | 46 | es_ES |
UDC.startPage | 252 | es_ES |
UDC.endPage | 262 | es_ES |
dc.identifier.doi | 10.1016/j.ijinfomgt.2018.10.006 |
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