Supervised autoencoder for fault detection and diagnosis in predictive maintenance of bearing ring grinding machine
Pietro Andrea MICIACCIA, Giovanni PASCOSCHI, Antonio DECATALDO, Domenico MONOPOLI, Concetta SEMERARO, Michele DASSISTI
Abstract. Modern manufacturing industry requires advanced maintenance strategies to ensure process reliability and efficiency. This study develops a deep learning-based algorithm using an autoencoder for fault detection and diagnosis in a bearing ring grinding machine. The proposed methodology follows a two-step classification framework: a binary classifier detects the presence of faults, while a multi-class classifier identifies the specific failure type. The dataset, sourced from the Swedish National Data Service, is analyzed using deep learning techniques to optimize predictive maintenance. Experimental results demonstrate high classification accuracy, enabling real-time monitoring and early identification of machine degradation. This approach enhances maintenance planning, reduces unexpected failures, and improves overall operational performance. Future research will focus on refining the estimation of Remaining Useful Life (RUL) to further optimize maintenance strategies.
Keywords
Grinding, Artificial Intelligence, Zero Defect
Published online 9/10/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Pietro Andrea MICIACCIA, Giovanni PASCOSCHI, Antonio DECATALDO, Domenico MONOPOLI, Concetta SEMERARO, Michele DASSISTI, Supervised autoencoder for fault detection and diagnosis in predictive maintenance of bearing ring grinding machine, Materials Research Proceedings, Vol. 57, pp 608-616, 2025
DOI: https://doi.org/10.21741/9781644903735-71
The article was published as article 71 of the book Italian Manufacturing Association Conference
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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