Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0

Fabian Berns, Markus Lange-Hegermann, Christian Beecks

2020

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 current challenges are when applying these probabilistic models to large-scale production data.

Paper Citation

in Harvard Style

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 - Volume 1: IN4PL, ISBN 978-989-758-476-3, pages 87-92. DOI: 10.5220/0010130300870092

in Bibtex Style

@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 - Volume 1: IN4PL,},
year={2020},
pages={87-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010130300870092},
isbn={978-989-758-476-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: 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