Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry

Fernandes, Marta; Canito, Alda; Bolón-Canedo, Verónica; Conceição, Luís; Praça, Isabel; Marreiros, Goreti

Use this link to cite:

http://hdl.handle.net/2183/37589

Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry

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BolonCanedo_Veronica_2019_Data_analysis_and_feature_selection_for_predictive_maintenance.pdf (2.96 MB)

Identifiers

URI: http://hdl.handle.net/2183/37589

DOI: 10.1016/j.ijinfomgt.2018.10.006

Publication date

2019-06

Authors

Fernandes, Marta

Canito, Alda

Bolón-Canedo, Verónica

Conceição, Luís

Praça, Isabel

Marreiros, Goreti

Bibliographic 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

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.