---
_id: '12812'
abstract:
- lang: eng
text: 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.
author:
- first_name: Fabian
full_name: Berns, Fabian
last_name: Berns
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Christian
full_name: Beecks, Christian
last_name: Beecks
citation:
ama: Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic
and Interpretable Anomaly Detection in Industry 4.0. (Panetto H, Madani K,
Smirnov A, eds.). SCITEPRESS - Science and Technology Publications; 2020:87-92.
doi:10.5220/0010130300870092
apa: Berns, F., Lange-Hegermann, M., & Beecks, C. (2020). Towards Gaussian Processes
for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto,
K. Madani, & A. Smirnov (Eds.), Proceedings of the International Conference
on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1
(pp. 87–92). SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0010130300870092
bjps: Berns F, Lange-Hegermann M and Beecks C (2020) Towards Gaussian
Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0,
Panetto H, Madani K and Smirnov A (eds). SCITEPRESS - Science and Technology Publications.
chicago: Berns, Fabian, Markus Lange-Hegermann, and Christian Beecks. Towards
Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry
4.0. Edited by H. Panetto, K. Madani, and A. Smirnov. Proceedings of the
International Conference on Innovative Intelligent Industrial Production and Logistics
IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications, 2020.
https://doi.org/10.5220/0010130300870092.
chicago-de: Berns, Fabian, Markus Lange-Hegermann und Christian Beecks. 2020. Towards
Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry
4.0. Hg. von H. Panetto, K. Madani, und A. Smirnov. Proceedings of the
International Conference on Innovative Intelligent Industrial Production and Logistics
IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications. doi:10.5220/0010130300870092,
.
din1505-2-1: 'Berns, Fabian ; Lange-Hegermann, Markus ; Beecks,
Christian ; Panetto, H. ;
Madani, K. ; Smirnov,
A. (Hrsg.): Towards Gaussian Processes for Automatic and Interpretable
Anomaly Detection in Industry 4.0 : SCITEPRESS - Science and Technology Publications,
2020'
havard: F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for
Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science
and Technology Publications, 2020.
ieee: 'F. Berns, M. Lange-Hegermann, and C. Beecks, Towards Gaussian Processes
for Automatic and Interpretable Anomaly Detection in Industry 4.0. SCITEPRESS
- Science and Technology Publications, 2020, pp. 87–92. doi: 10.5220/0010130300870092.'
mla: Berns, Fabian, et al. “Towards Gaussian Processes for Automatic and Interpretable
Anomaly Detection in Industry 4.0.” Proceedings of the International Conference
on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1,
edited by H. Panetto et al., SCITEPRESS - Science and Technology Publications,
2020, pp. 87–92, https://doi.org/10.5220/0010130300870092.
short: F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic
and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and
Technology Publications, 2020.
ufg: 'Berns, Fabian/Lange-Hegermann, Markus/Beecks, Christian: Towards Gaussian
Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, hg.
von Panetto, H./Madani, K./Smirnov, A., o. O. 2020.'
van: Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic
and Interpretable Anomaly Detection in Industry 4.0. Panetto H, Madani K, Smirnov
A, editors. Proceedings of the International Conference on Innovative Intelligent
Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and
Technology Publications; 2020.
conference:
end_date: 2020-11-04
location: Budapest, HUNGARY
name: International Conference on Innovative Intelligent Industrial Production and
Logistics (IN4PL)
start_date: 2020-11-02
date_created: 2025-04-17T06:20:07Z
date_updated: 2025-06-26T13:31:38Z
department:
- _id: DEP5000
doi: 10.5220/0010130300870092
editor:
- first_name: H.
full_name: Panetto, H.
last_name: Panetto
- first_name: K.
full_name: Madani, K.
last_name: Madani
- first_name: A.
full_name: Smirnov, A.
last_name: Smirnov
keyword:
- Anomaly Detection
- Gaussian Processes
- Explainable Machine Learning
- Industry 4.0
language:
- iso: eng
page: 87-92
publication: ' Proceedings of the International Conference on Innovative Intelligent
Industrial Production and Logistics IN4PL - Volume 1'
publication_identifier:
isbn:
- 978-989-758-476-3
publication_status: published
publisher: SCITEPRESS - Science and Technology Publications
status: public
title: Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection
in Industry 4.0
type: conference_editor_article
user_id: '83781'
year: '2020'
...