Review article

https://doi.org/10.31803/tg-20240518145453

Teaching Predictive Maintenance using Industrial AI Tools

Ihor Savchenko ; FH Joanneum, Werk-VI-Straße 46, 8605 Kapfenberg, Austria *
Herbert Fleck ; HITEL GmbH, Redtenbachergasse 25/1/12, 1160 Vienna, Austria
Peter Novotny ; FH Joanneum, Werk-VI-Straße 46, 8605 Kapfenberg, Austria
Helmut Ropin ; FH Joanneum, Werk-VI-Straße 46, 8605 Kapfenberg, Austria

* Corresponding author.

Abstract

A new concept of an educational tool using predictive maintenance techniques is proposed. The tool is a part of a larger 'learning and research factory' that aims to enhance the current educational processes for industrial engineering students. The factory is organized as close as possible to the real business situation, so the students "learn by doing", gaining both theoretical knowledge and practical skills. Two use cases presented by the authors intended to effectively teach the principles of predictive maintenance. Basic elements of data science, machine learning and statistical analysis are used to prognose possible anomalies in the production process and react actively before a harmful event occurs. The authors outline ways for further development of the tool, including using it for other educational purposes.

Keywords

AI; anomalies; digital twin; machine learning; predictive maintenance

Hrčak ID:

335279

URI

https://hrcak.srce.hr/335279

Publication date:

15.12.2025.

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