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Assessment of Explainable Anomaly Detection for Monitoring of Cold Rolling Process

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Computational Science – ICCS 2024 (ICCS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14836))

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Abstract

The detection and explanation of anomalies within the industrial context remains a difficult task, which requires the use of well-designed methods. In this study, we focus on evaluating the performance of Explainable Anomaly Detection (XAD) algorithms in the context of a complex industrial process, specifically cold rolling. We train several state-of-the-art anomaly detection algorithms on the synthetic data from the cold rolling process and optimize their hyperparameters to maximize its predictive capabilities. Then we employ various model-agnostic Explainable AI (XAI) methods to generate explanations for the abnormal observations. The explanations are evaluated using a set of XAI metrics specifically selected for the anomaly detection task in industrial setting. The results provide insights into the impact of the selection of both machine learning and XAI methods on the overall performance of the model, emphasizing the importance of interpretability in industrial applications. For the detection of anomalies in cold rolling, we found that autoencoder-based approaches outperformed other methods, with the SHAP method providing the best explanations according to the evaluation metrics used.

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Acknowledgements

Project XPM is supported by the National Science Centre, Poland (2020/02/Y/ST6/00070), under CHIST-ERA IV programme, which has received funding from the EU Horizon 2020 Research and Innovation Programme, under Grant Agreement no. 857925.

Author information

Authors and Affiliations

  1. Department of Applied Computer Science, AGH University of Science and Technology, 30-059, Krakow, Poland

    Jakub Jakubowski & Przemysław Stanisz

  2. Faculty of Physics, Astronomy and Applied Computer Science, Institute of Applied Computer Science, and Jagiellonian Human-Centered AI Lab (JAHCAI), and Mark Kac Center for Complex Systems Research, Jagiellonian University, Ul. Prof. Stanisława Łojasiewicza 11, 30-348, Krakow, Poland

    Szymon Bobek & Grzegorz J. Nalepa

Authors
  1. Jakub Jakubowski
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  2. Przemysław Stanisz
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  3. Szymon Bobek
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  4. Grzegorz J. Nalepa
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Corresponding author

Correspondence to Jakub Jakubowski .

Editor information

Editors and Affiliations

  1. University of Malaga, Malaga, Spain

    Leonardo Franco

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Jakubowski, J., Stanisz, P., Bobek, S., Nalepa, G.J. (2024). Assessment of Explainable Anomaly Detection for Monitoring of Cold Rolling Process. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14836. Springer, Cham. https://doi.org/10.1007/978-3-031-63775-9_24

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  • DOI: https://doi.org/10.1007/978-3-031-63775-9_24

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