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

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

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.

Suggested Citation

  • Fernandes, Marta & Canito, Alda & Bolón-Canedo, Verónica & Conceição, Luís & Praça, Isabel & Marreiros, Goreti, 2019. "Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry," International Journal of Information Management, Elsevier, vol. 46(C), pages 252-262.
    • Handle: RePEc:eee:ininma:v:46:y:2019:i:c:p:252-262
    DOI: 10.1016/j.ijinfomgt.2018.10.006
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    References listed on IDEAS

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    1. Aboelmaged, Mohamed Gamal, 2014. "Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms," International Journal of Information Management, Elsevier, vol. 34(5), pages 639-651.
    2. Muller, Alexandre & Crespo Marquez, Adolfo & Iung, Benoît, 2008. "On the concept of e-maintenance: Review and current research," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1165-1187.
    3. Raguseo, Elisabetta, 2018. "Big data technologies: An empirical investigation on their adoption, benefits and risks for companies," International Journal of Information Management, Elsevier, vol. 38(1), pages 187-195.
    4. Santos, Maribel Yasmina & Oliveira e Sá, Jorge & Andrade, Carina & Vale Lima, Francisca & Costa, Eduarda & Costa, Carlos & Martinho, Bruno & Galvão, João, 2017. "A Big Data system supporting Bosch Braga Industry 4.0 strategy," International Journal of Information Management, Elsevier, vol. 37(6), pages 750-760.
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