Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry
Use este enlace para citar
http://hdl.handle.net/2183/37589
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial_NoDerivs 4.0 Internationsl (CC BY-NC-ND 4.0)
Colecciones
- GI-LIDIA - Artigos [63]
Metadatos
Mostrar el registro completo del ítemTítulo
Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industryAutor(es)
Fecha
2019-06Cita bibliográfica
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
Resumen
[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.
Palabras clave
Predictive maintenance
Data analysis
Feature selection
Rule-based model
Data analysis
Feature selection
Rule-based model
Versión del editor
Derechos
Attribution-NonCommercial_NoDerivs 4.0 Internationsl (CC BY-NC-ND 4.0)
ISSN
0268-4012