---
_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'
...
---
_id: '12808'
abstract:
- lang: eng
text: Along with the constantly increasing complexity of industrial automation systems,
machine learning methods have been widely applied to detecting abnormal states
in such systems. Anomaly detection tasks can be treated as one-class classification
problems in machine learning. Geometric methods can give an intuitive solution
to such problems. In this paper, we propose a new geometric structure, oriented
non-convex hulls, to represent decision boundaries used for one-class classification.
Based on this geometric structure, a novel boundary based one-class classification
algorithm is developed to solve the anomaly detection problem. Compared with traditional
boundary-based approaches such as convex hulls based methods and one-class support
vector machines, the proposed approach can better reflect the true geometry of
target data and needs little effort for parameter tuning. The effectiveness of
this approach is evaluated with artificial and real world data sets to solve the
anomaly detection problem in Cyber-Physical-Production-Systems (CPPS). The evaluation
results also show that the proposed approach has higher generality than the used
baseline algorithms.
article_number: '103301'
author:
- first_name: Peng
full_name: Li, Peng
id: '58937'
last_name: Li
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
citation:
ama: Li P, Niggemann O. Non-convex hull based anomaly detection in CPPS. Engineering
Applications of Artificial Intelligence. 2019;87. doi:10.1016/j.engappai.2019.103301
apa: Li, P., & Niggemann, O. (2019). Non-convex hull based anomaly detection
in CPPS. Engineering Applications of Artificial Intelligence, 87,
Article 103301. https://doi.org/10.1016/j.engappai.2019.103301
bjps: Li P and Niggemann O (2019) Non-Convex Hull Based Anomaly Detection
in CPPS. Engineering Applications of Artificial Intelligence 87.
chicago: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection
in CPPS.” Engineering Applications of Artificial Intelligence 87 (2019).
https://doi.org/10.1016/j.engappai.2019.103301.
chicago-de: Li, Peng und Oliver Niggemann. 2019. Non-convex hull based anomaly detection
in CPPS. Engineering Applications of Artificial Intelligence 87. doi:10.1016/j.engappai.2019.103301,
.
din1505-2-1: 'Li, Peng ; Niggemann,
Oliver: Non-convex hull based anomaly detection in CPPS. In: Engineering
Applications of Artificial Intelligence Bd. 87. Amsterdam [u.a.], Elsevier
BV (2019)'
havard: P. Li, O. Niggemann, Non-convex hull based anomaly detection in CPPS, Engineering
Applications of Artificial Intelligence. 87 (2019).
ieee: 'P. Li and O. Niggemann, “Non-convex hull based anomaly detection in CPPS,”
Engineering Applications of Artificial Intelligence, vol. 87, Art. no.
103301, 2019, doi: 10.1016/j.engappai.2019.103301.'
mla: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection in
CPPS.” Engineering Applications of Artificial Intelligence, vol. 87, 103301,
2019, https://doi.org/10.1016/j.engappai.2019.103301.
short: P. Li, O. Niggemann, Engineering Applications of Artificial Intelligence
87 (2019).
ufg: 'Li, Peng/Niggemann, Oliver: Non-convex hull based anomaly detection
in CPPS, in: Engineering Applications of Artificial Intelligence 87 (2019).'
van: Li P, Niggemann O. Non-convex hull based anomaly detection in CPPS. Engineering
Applications of Artificial Intelligence. 2019;87.
date_created: 2025-04-16T09:51:12Z
date_updated: 2025-06-26T13:34:10Z
department:
- _id: DEP5023
doi: 10.1016/j.engappai.2019.103301
external_id:
isi:
- '000506715100040'
intvolume: ' 87'
isi: '1'
keyword:
- One-class classification
- n-dimensional oriented non-convex hull
- Anomaly detection
- CPPS
language:
- iso: eng
place: Amsterdam [u.a.]
publication: Engineering Applications of Artificial Intelligence
publication_identifier:
eissn:
- 1873-6769
issn:
- 0952-1976
publication_status: published
publisher: Elsevier BV
status: public
title: Non-convex hull based anomaly detection in CPPS
type: scientific_journal_article
user_id: '83781'
volume: 87
year: '2019'
...
---
_id: '2167'
abstract:
- lang: eng
text: "Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry
and politics: By applying new IT and new computer science solutions, production
systems will become more adaptable, more resource ef- ficient and more user friendly.
The analysis and diagnosis of such systems is a major part of this trend: Plants
should detect automatically wear, faults and suboptimal configurations. This paper
reflects the current state-of- the-art in diagnosis against the requirements of
CPPSs, identifies three main gaps and gives application scenarios to outline first
ideas for potential solutions to close these gaps.\r\n"
author:
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Niggemann O, Lohweg V. On the Diagnosis of Cyber-Physical Production Systems
- State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial
Intelligence (AAAI-15). Austin, Texas, USA; 2015.'
apa: Niggemann, O., & Lohweg, V. (2015). On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda. In Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA.
bjps: Niggemann O and Lohweg V (2015) On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda. Twenty-Ninth Conference
on Artificial Intelligence (AAAI-15). Austin, Texas, USA.
chicago: Niggemann, Oliver, and Volker Lohweg. “On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda.” In Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA, 2015.
chicago-de: 'Niggemann, Oliver und Volker Lohweg. 2015. On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda. In: Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA.'
din1505-2-1: 'Niggemann, Oliver ;
Lohweg, Volker: On the Diagnosis
of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In:
Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas,
USA, 2015'
havard: 'O. Niggemann, V. Lohweg, On the Diagnosis of Cyber-Physical Production
Systems - State-of-the-Art and Research Agenda, in: Twenty-Ninth Conference on
Artificial Intelligence (AAAI-15), Austin, Texas, USA, 2015.'
ieee: O. Niggemann and V. Lohweg, “On the Diagnosis of Cyber-Physical Production
Systems - State-of-the-Art and Research Agenda,” in Twenty-Ninth Conference
on Artificial Intelligence (AAAI-15), 2015.
mla: Niggemann, Oliver, and Volker Lohweg. “On the Diagnosis of Cyber-Physical Production
Systems - State-of-the-Art and Research Agenda.” Twenty-Ninth Conference on
Artificial Intelligence (AAAI-15), 2015.
short: 'O. Niggemann, V. Lohweg, in: Twenty-Ninth Conference on Artificial Intelligence
(AAAI-15), Austin, Texas, USA, 2015.'
ufg: 'Niggemann, Oliver/Lohweg, Volker (2015): On the Diagnosis of Cyber-Physical
Production Systems - State-of-the-Art and Research Agenda, in: Twenty-Ninth
Conference on Artificial Intelligence (AAAI-15), Austin, Texas, USA.'
van: 'Niggemann O, Lohweg V. On the Diagnosis of Cyber-Physical Production Systems
- State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial
Intelligence (AAAI-15). Austin, Texas, USA; 2015.'
date_created: 2019-12-04T12:43:12Z
date_updated: 2023-03-15T13:49:39Z
department:
- _id: DEP5023
keyword:
- Cyber-Physical Systems
- Machine Learning
- Diagnosis
- Anomaly Detection
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9530/9691
oa: '1'
place: Austin, Texas, USA
publication: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15)
status: public
title: On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and
Research Agenda
type: conference
user_id: '68554'
year: 2015
...