Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry. Fernandes, M., Canito, A., Bolón-Canedo, V., Conceição, L., Praça, I., & Marreiros, G. International Journal of Information Management, 46:252 – 262, 2019. ![Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{fernandes_data_2019,
title = {Data analysis and feature selection for predictive maintenance: {A} case-study in the metallurgic industry},
volume = {46},
issn = {0268-4012},
url = {http://www.sciencedirect.com/science/article/pii/S0268401218304699},
doi = {https://doi.org/10.1016/j.ijinfomgt.2018.10.006},
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.},
journal = {International Journal of Information Management},
author = {Fernandes, Marta and Canito, Alda and Bolón-Canedo, Verónica and Conceição, Luís and Praça, Isabel and Marreiros, Goreti},
year = {2019},
keywords = {Data analysis, Feature selection, Predictive maintenance, Rule-based model},
pages = {252 -- 262},
}
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