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Predictive Maintenance with Linguistic Text Mining

Author

Listed:
  • Alberto Postiglione

    (Department of Business Science and Management & Innovation Systems, University of Salerno, Via San Giovanni Paolo II, 132, 84084 Fisciano, Italy
    These authors contributed equally to this work.)

  • Mario Monteleone

    (Department of Political and Communication Sciences, University of Salerno, Via San Giovanni Paolo II, 132, 84084 Fisciano, Italy
    These authors contributed equally to this work.)

Abstract

The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency.

Suggested Citation

  • Alberto Postiglione & Mario Monteleone, 2024. "Predictive Maintenance with Linguistic Text Mining," Mathematics, MDPI, vol. 12(7), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1089-:d:1369982
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    References listed on IDEAS

    as
    1. Ignacio Rodríguez-Rodríguez & José-Víctor Rodríguez & Niloofar Shirvanizadeh & Andrés Ortiz & Domingo-Javier Pardo-Quiles, 2021. "Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
    2. Florian, Eleonora & Sgarbossa, Fabio & Zennaro, Ilenia, 2021. "Machine learning-based predictive maintenance: A cost-oriented model for implementation," International Journal of Production Economics, Elsevier, vol. 236(C).
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