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Authors: Fabian Berns 1 ; Markus Lange-Hegermann 2 and Christian Beecks 1

Affiliations: 1 Department of Computer Science, University of Münster, Germany ; 2 Department of Electrical Engineering and Computer Science, OWL University of Applied Sciences and Arts, Lemgo, Germany

ISBN: 978-989-758-476-3

Keyword(s): Anomaly Detection, Gaussian Processes, Explainable Machine Learning, Industry 4.0.

Abstract: Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the cur rent challenges are when applying these probabilistic models to large-scale production data. (More)

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Paper citation in several formats:
Berns, F.; Lange-Hegermann, M. and Beecks, C. (2020). Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. In Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL, ISBN 978-989-758-476-3, pages 87-92. DOI: 10.5220/0010130300870092

@conference{in4pl20,
author={Fabian Berns. and Markus Lange{-}Hegermann. and Christian Beecks.},
title={Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0},
booktitle={Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL,},
year={2020},
pages={87-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010130300870092},
isbn={978-989-758-476-3},
}

TY - CONF

JO - Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL,
TI - Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0
SN - 978-989-758-476-3
AU - Berns, F.
AU - Lange-Hegermann, M.
AU - Beecks, C.
PY - 2020
SP - 87
EP - 92
DO - 10.5220/0010130300870092

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