---
_id: '64787'
abstract:
- lang: eng
text: This study proposes a fault diagnostics methodology that addresses the challenges
posed by highly imbalanced datasets typical of railway applications, where faulty
conditions constitute the minority class. Fault diagnostics is performed from
the component level upward, considering each sensor’s proximity to its respective
critical component. Advanced signal analysis, feature engineering, and automated
data-driven model generation techniques were explored to achieve comprehensive
diagnostics, such that the model development process accounts for variations in
the operating conditions and differing levels of information availability. The
proposed methodology is evaluated on datasets from the MONOCAB, for scenarios
with limited faulty instances and on the Beijing 2024 IEEE PHM Conference data
challenge, which focused on fault diagnostics of railway systems under various
fault modes and operating conditions.
article_number: '1'
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Alexander
full_name: Löwen, Alexander
id: '47233'
last_name: Löwen
- first_name: Raphael
full_name: Hanselle, Raphael
last_name: Hanselle
- first_name: Thomas
full_name: Rief, Thomas
last_name: Rief
- first_name: Maximilian
full_name: Beck, Maximilian
last_name: Beck
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Löwen A, Hanselle R, Rief T, Beck M, Sextro W. Multilevel
fault diagnostics for railway applications using limited historical data. In:
PHM Society Asia-Pacific Conference. Vol 5. ; 2025. doi:10.36001/phmap.2025.v5i1.4449'
apa: Aimiyekagbon, O. K., Löwen, A., Hanselle, R., Rief, T., Beck, M., & Sextro,
W. (2025). Multilevel fault diagnostics for railway applications using limited
historical data. PHM Society Asia-Pacific Conference, 5, Article
1. https://doi.org/10.36001/phmap.2025.v5i1.4449
bibtex: '@inproceedings{Aimiyekagbon_Löwen_Hanselle_Rief_Beck_Sextro_2025, title={Multilevel
fault diagnostics for railway applications using limited historical data}, volume={5},
DOI={10.36001/phmap.2025.v5i1.4449},
number={1}, booktitle={PHM Society Asia-Pacific Conference}, author={Aimiyekagbon,
Osarenren Kennedy and Löwen, Alexander and Hanselle, Raphael and Rief, Thomas
and Beck, Maximilian and Sextro, Walter}, year={2025} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Alexander Löwen, Raphael Hanselle, Thomas
Rief, Maximilian Beck, and Walter Sextro. “Multilevel Fault Diagnostics for Railway
Applications Using Limited Historical Data.” In PHM Society Asia-Pacific Conference,
Vol. 5, 2025. https://doi.org/10.36001/phmap.2025.v5i1.4449.
ieee: 'O. K. Aimiyekagbon, A. Löwen, R. Hanselle, T. Rief, M. Beck, and W. Sextro,
“Multilevel fault diagnostics for railway applications using limited historical
data,” in PHM Society Asia-Pacific Conference, 2025, vol. 5, doi: 10.36001/phmap.2025.v5i1.4449.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Multilevel Fault Diagnostics for Railway
Applications Using Limited Historical Data.” PHM Society Asia-Pacific Conference,
vol. 5, 1, 2025, doi:10.36001/phmap.2025.v5i1.4449.
short: 'O.K. Aimiyekagbon, A. Löwen, R. Hanselle, T. Rief, M. Beck, W. Sextro, in:
PHM Society Asia-Pacific Conference, 2025.'
date_created: 2026-02-27T20:41:54Z
date_updated: 2026-02-27T20:46:44Z
department:
- _id: '151'
doi: 10.36001/phmap.2025.v5i1.4449
intvolume: ' 5'
keyword:
- MONOCAB
- Beijing Data Challenge
- Diagnostics of railway systems
language:
- iso: eng
project:
- _id: '1355'
name: enableATO – Automatisierter Bahnverkehr als Backbone für eine nachhaltige,
vernetzte Mobilität im ländlichen Raum
publication: PHM Society Asia-Pacific Conference
publication_status: published
quality_controlled: '1'
status: public
title: Multilevel fault diagnostics for railway applications using limited historical
data
type: conference
user_id: '9557'
volume: 5
year: '2025'
...
---
_id: '63193'
abstract:
- lang: eng
text: The integration of data-driven models and specifically machine learning for
conditon monitoring and predictive maintenance into companies, especially small
and medium-sized enterprises, offers significant opportunities in reducing costs,
operating more sustainably, and maintaining long-term competitiveness. However,
many small and medium-sized enterprises lack the necessary resources and expertise
to derive knowledge from data and integrate their own machine learning based solutions.
To address this challenge, a framework is presented that enables the automated
generation of data-driven models with a particular focus on condition monitoring
and predictive maintenance, but applicable to other use cases as well. Using a
dataset from the 2022 data challenge of the prognostics and health management
society, it is demonstrated that the framework can generate high-performing models,
achieving F1-scores up to 0.998, exemplarily for a classification task.
author:
- first_name: Alexander
full_name: Löwen, Alexander
id: '47233'
last_name: Löwen
- first_name: Dennis
full_name: Quirin, Dennis
last_name: Quirin
- first_name: Marc
full_name: Hesse, Marc
last_name: Hesse
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Löwen A, Quirin D, Hesse M, Aimiyekagbon OK, Sextro W. Facilitating the Automated
Generation of Data-Driven Models for the Diagnostics and Prognostics of Technical
Systems. In: 2025 IEEE 30th International Conference on Emerging Technologies
and Factory Automation (ETFA). IEEE; 2025. doi:10.1109/etfa65518.2025.11205799'
apa: Löwen, A., Quirin, D., Hesse, M., Aimiyekagbon, O. K., & Sextro, W. (2025).
Facilitating the Automated Generation of Data-Driven Models for the Diagnostics
and Prognostics of Technical Systems. 2025 IEEE 30th International Conference
on Emerging Technologies and Factory Automation (ETFA). 2025 IEEE 30th International
Conference on Emerging Technologies and Factory Automation (ETFA), Porto. https://doi.org/10.1109/etfa65518.2025.11205799
bibtex: '@inproceedings{Löwen_Quirin_Hesse_Aimiyekagbon_Sextro_2025, title={Facilitating
the Automated Generation of Data-Driven Models for the Diagnostics and Prognostics
of Technical Systems}, DOI={10.1109/etfa65518.2025.11205799},
booktitle={2025 IEEE 30th International Conference on Emerging Technologies and
Factory Automation (ETFA)}, publisher={IEEE}, author={Löwen, Alexander and Quirin,
Dennis and Hesse, Marc and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter},
year={2025} }'
chicago: Löwen, Alexander, Dennis Quirin, Marc Hesse, Osarenren Kennedy Aimiyekagbon,
and Walter Sextro. “Facilitating the Automated Generation of Data-Driven Models
for the Diagnostics and Prognostics of Technical Systems.” In 2025 IEEE 30th
International Conference on Emerging Technologies and Factory Automation (ETFA).
IEEE, 2025. https://doi.org/10.1109/etfa65518.2025.11205799.
ieee: 'A. Löwen, D. Quirin, M. Hesse, O. K. Aimiyekagbon, and W. Sextro, “Facilitating
the Automated Generation of Data-Driven Models for the Diagnostics and Prognostics
of Technical Systems,” presented at the 2025 IEEE 30th International Conference
on Emerging Technologies and Factory Automation (ETFA), Porto, 2025, doi: 10.1109/etfa65518.2025.11205799.'
mla: Löwen, Alexander, et al. “Facilitating the Automated Generation of Data-Driven
Models for the Diagnostics and Prognostics of Technical Systems.” 2025 IEEE
30th International Conference on Emerging Technologies and Factory Automation
(ETFA), IEEE, 2025, doi:10.1109/etfa65518.2025.11205799.
short: 'A. Löwen, D. Quirin, M. Hesse, O.K. Aimiyekagbon, W. Sextro, in: 2025 IEEE
30th International Conference on Emerging Technologies and Factory Automation
(ETFA), IEEE, 2025.'
conference:
location: Porto
name: 2025 IEEE 30th International Conference on Emerging Technologies and Factory
Automation (ETFA)
date_created: 2025-12-18T09:07:38Z
date_updated: 2025-12-18T09:12:48Z
department:
- _id: '151'
doi: 10.1109/etfa65518.2025.11205799
language:
- iso: eng
project:
- _id: '1483'
name: Industrie 4.0 Ökosystem für den automatisierten Einsatz von datengetriebenen
Services (I4.0AutoServ)
publication: 2025 IEEE 30th International Conference on Emerging Technologies and
Factory Automation (ETFA)
publication_status: published
publisher: IEEE
related_material:
link:
- relation: confirmation
url: https://ieeexplore.ieee.org/document/11205799
status: public
title: Facilitating the Automated Generation of Data-Driven Models for the Diagnostics
and Prognostics of Technical Systems
type: conference
user_id: '47233'
year: '2025'
...
---
_id: '56862'
author:
- first_name: Magnus
full_name: Redeker, Magnus
last_name: Redeker
- first_name: Dennis
full_name: Quirin, Dennis
last_name: Quirin
- first_name: Rafael
full_name: Schroeder, Rafael
last_name: Schroeder
- first_name: Tobias
full_name: Klausmann, Tobias
last_name: Klausmann
- first_name: Alexander
full_name: Löwen, Alexander
id: '47233'
last_name: Löwen
- first_name: Alexander
full_name: Wollbrink, Alexander
last_name: Wollbrink
- first_name: Heiko
full_name: Stichweh, Heiko
last_name: Stichweh
- first_name: Simon
full_name: Althoff, Simon
last_name: Althoff
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
- first_name: Marc
full_name: Hesse, Marc
last_name: Hesse
citation:
ama: 'Redeker M, Quirin D, Schroeder R, et al. Towards a One-Stop-Shop Solution
for the Application of Data-Driven Value-Adding Services in Production. In: 2024
IEEE 29th International Conference on Emerging Technologies and Factory Automation
(ETFA). Vol 13. IEEE; 2024. doi:10.1109/etfa61755.2024.10711095'
apa: Redeker, M., Quirin, D., Schroeder, R., Klausmann, T., Löwen, A., Wollbrink,
A., Stichweh, H., Althoff, S., Bender, A., Sextro, W., & Hesse, M. (2024).
Towards a One-Stop-Shop Solution for the Application of Data-Driven Value-Adding
Services in Production. 2024 IEEE 29th International Conference on Emerging
Technologies and Factory Automation (ETFA), 13. https://doi.org/10.1109/etfa61755.2024.10711095
bibtex: '@inproceedings{Redeker_Quirin_Schroeder_Klausmann_Löwen_Wollbrink_Stichweh_Althoff_Bender_Sextro_et
al._2024, title={Towards a One-Stop-Shop Solution for the Application of Data-Driven
Value-Adding Services in Production}, volume={13}, DOI={10.1109/etfa61755.2024.10711095},
booktitle={2024 IEEE 29th International Conference on Emerging Technologies and
Factory Automation (ETFA)}, publisher={IEEE}, author={Redeker, Magnus and Quirin,
Dennis and Schroeder, Rafael and Klausmann, Tobias and Löwen, Alexander and Wollbrink,
Alexander and Stichweh, Heiko and Althoff, Simon and Bender, Amelie and Sextro,
Walter and et al.}, year={2024} }'
chicago: Redeker, Magnus, Dennis Quirin, Rafael Schroeder, Tobias Klausmann, Alexander
Löwen, Alexander Wollbrink, Heiko Stichweh, et al. “Towards a One-Stop-Shop Solution
for the Application of Data-Driven Value-Adding Services in Production.” In 2024
IEEE 29th International Conference on Emerging Technologies and Factory Automation
(ETFA), Vol. 13. IEEE, 2024. https://doi.org/10.1109/etfa61755.2024.10711095.
ieee: 'M. Redeker et al., “Towards a One-Stop-Shop Solution for the Application
of Data-Driven Value-Adding Services in Production,” in 2024 IEEE 29th International
Conference on Emerging Technologies and Factory Automation (ETFA), 2024, vol.
13, doi: 10.1109/etfa61755.2024.10711095.'
mla: Redeker, Magnus, et al. “Towards a One-Stop-Shop Solution for the Application
of Data-Driven Value-Adding Services in Production.” 2024 IEEE 29th International
Conference on Emerging Technologies and Factory Automation (ETFA), vol. 13,
IEEE, 2024, doi:10.1109/etfa61755.2024.10711095.
short: 'M. Redeker, D. Quirin, R. Schroeder, T. Klausmann, A. Löwen, A. Wollbrink,
H. Stichweh, S. Althoff, A. Bender, W. Sextro, M. Hesse, in: 2024 IEEE 29th International
Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2024.'
date_created: 2024-11-04T12:30:48Z
date_updated: 2024-11-04T12:34:48Z
doi: 10.1109/etfa61755.2024.10711095
intvolume: ' 13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/document/10711095
oa: '1'
publication: 2024 IEEE 29th International Conference on Emerging Technologies and
Factory Automation (ETFA)
publication_status: published
publisher: IEEE
status: public
title: Towards a One-Stop-Shop Solution for the Application of Data-Driven Value-Adding
Services in Production
type: conference
user_id: '47233'
volume: 13
year: '2024'
...
---
_id: '55568'
abstract:
- lang: eng
text: Historical condition monitoring data from technical systems can be
utilized to develop data-driven models for predicting the remaining useful life
(RUL) of similar systems, whereas the Health Index (HI) often is a crucial component.
The development of robust and accurate models requires meaningful features that
reflect the system’s degradation process, enabling an accurate prediction of the
system's HI. Traditionally, the identification of those is supported by one of
various feature ranking methods. In literature, feature interdependencies and
their transferability across various similar systems are not sufficiently considered
in feature selection, exacerbating the challenge of HI prediction posed by the
scarcity of data and system diversity in real-world applications. This work addresses
this gaps by demonstrating how filter-based feature selection, incorporating failure
thresholds and cross correlations, enhances feature selection leading to improved
HI prediction. The proposed methodology is applied to a novel dataset* obtained
from run-to-failure experiments on geared motors conducted as part of this study,
which presents the aforementioned challenges. It is revealed that classical feature
selection, consisting of feature ranking only, leaves potential untapped, which
is utilized by the proposed selection methodology. It is shown that the proposed
feature selection methodology leads to the best result with a RMSE of 0.14 in
predicting the HI of a constructive different gearbox, while the features, determined
by classical feature selection, lead to a RMSE of 0.19 at best.
author:
- first_name: Alexander
full_name: Löwen, Alexander
id: '47233'
last_name: Löwen
- first_name: Peter
full_name: Wissbrock, Peter
last_name: Wissbrock
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: Löwen A, Wissbrock P, Bender A, Sextro W. Filter-based feature selection for
prognostics incorporating cross correlations and failure thresholds. PHM Society
European Conference. 2024;8(1):955-964. doi:10.36001/phme.2024.v8i1.4075
apa: Löwen, A., Wissbrock, P., Bender, A., & Sextro, W. (2024). Filter-based
feature selection for prognostics incorporating cross correlations and failure
thresholds. PHM Society European Conference, 8(1), 955–964. https://doi.org/10.36001/phme.2024.v8i1.4075
bibtex: '@article{Löwen_Wissbrock_Bender_Sextro_2024, title={Filter-based feature
selection for prognostics incorporating cross correlations and failure thresholds},
volume={8}, DOI={10.36001/phme.2024.v8i1.4075},
number={1}, journal={PHM Society European Conference}, publisher={PHM Society},
author={Löwen, Alexander and Wissbrock, Peter and Bender, Amelie and Sextro, Walter},
year={2024}, pages={955–964} }'
chicago: 'Löwen, Alexander, Peter Wissbrock, Amelie Bender, and Walter Sextro. “Filter-Based
Feature Selection for Prognostics Incorporating Cross Correlations and Failure
Thresholds.” PHM Society European Conference 8, no. 1 (2024): 955–64. https://doi.org/10.36001/phme.2024.v8i1.4075.'
ieee: 'A. Löwen, P. Wissbrock, A. Bender, and W. Sextro, “Filter-based feature selection
for prognostics incorporating cross correlations and failure thresholds,” PHM
Society European Conference, vol. 8, no. 1, pp. 955–964, 2024, doi: 10.36001/phme.2024.v8i1.4075.'
mla: Löwen, Alexander, et al. “Filter-Based Feature Selection for Prognostics Incorporating
Cross Correlations and Failure Thresholds.” PHM Society European Conference,
vol. 8, no. 1, PHM Society, 2024, pp. 955–64, doi:10.36001/phme.2024.v8i1.4075.
short: A. Löwen, P. Wissbrock, A. Bender, W. Sextro, PHM Society European Conference
8 (2024) 955–964.
conference:
end_date: 2024-07-05
location: Prague
name: 8th European Conference of the Prognostics and Health Management Society 2024
start_date: 2024-07-03
date_created: 2024-08-08T09:22:33Z
date_updated: 2025-02-10T10:58:57Z
department:
- _id: '151'
doi: 10.36001/phme.2024.v8i1.4075
intvolume: ' 8'
issue: '1'
language:
- iso: eng
page: 955-964
publication: PHM Society European Conference
publication_identifier:
isbn:
- 978-1-936263-40-0
publication_status: published
publisher: PHM Society
quality_controlled: '1'
related_material:
link:
- relation: confirmation
url: https://papers.phmsociety.org/index.php/phme/article/download/4075/2477
status: public
title: Filter-based feature selection for prognostics incorporating cross correlations
and failure thresholds
type: journal_article
user_id: '47233'
volume: 8
year: '2024'
...
---
_id: '47116'
abstract:
- lang: eng
text: This paper presents a comprehensive study on diagnosing a spacecraft propulsion
system utilizing data provided by the Prognostics and Health Management (PHM)
society, specifically obtained as part of the Asia-Pacific PHM conference’s data
challenge 2023. The objective of the challenge is to identify and diagnose known
faults as well as unknown anomalies in the spacecraft’s propulsion system, which
is critical for ensuring the spacecraft’s proper functionality and safety. To
address this challenge, the proposed method follows a systematic approach of feature
extraction, feature selection, and model development. The models employed in this
study are kMeans clustering and decision trees combined to ensembles, enriched
with expert knowledge. With the method presented, our team was capable of reaching
high accuracy in identifying anomalies as well as diagnosing faults, resulting
in attaining the seventh place with a score of 93.08 %.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Alexander
full_name: Löwen, Alexander
id: '47233'
last_name: Löwen
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Löwen A, Bender A, Muth L, Sextro W. Expert-Informed Hierarchical
Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System. In: Proceedings
of the Asia Pacific Conference of the PHM Society 2023 . Vol 4. ; 2023. doi:10.36001/phmap.2023.v4i1.3596'
apa: Aimiyekagbon, O. K., Löwen, A., Bender, A., Muth, L., & Sextro, W. (2023).
Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft
Propulsion System. Proceedings of the Asia Pacific Conference of the PHM Society
2023 , 4(1). https://doi.org/10.36001/phmap.2023.v4i1.3596
bibtex: '@inproceedings{Aimiyekagbon_Löwen_Bender_Muth_Sextro_2023, title={Expert-Informed
Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System},
volume={4}, DOI={10.36001/phmap.2023.v4i1.3596},
number={1}, booktitle={Proceedings of the Asia Pacific Conference of the PHM Society
2023 }, author={Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Bender,
Amelie and Muth, Lars and Sextro, Walter}, year={2023} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Alexander Löwen, Amelie Bender, Lars Muth,
and Walter Sextro. “Expert-Informed Hierarchical Diagnostics of Multiple Fault
Modes of a Spacecraft Propulsion System.” In Proceedings of the Asia Pacific
Conference of the PHM Society 2023 , Vol. 4, 2023. https://doi.org/10.36001/phmap.2023.v4i1.3596.
ieee: 'O. K. Aimiyekagbon, A. Löwen, A. Bender, L. Muth, and W. Sextro, “Expert-Informed
Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System,”
in Proceedings of the Asia Pacific Conference of the PHM Society 2023 ,
2023, vol. 4, no. 1, doi: 10.36001/phmap.2023.v4i1.3596.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Expert-Informed Hierarchical Diagnostics
of Multiple Fault Modes of a Spacecraft Propulsion System.” Proceedings of
the Asia Pacific Conference of the PHM Society 2023 , vol. 4, no. 1, 2023,
doi:10.36001/phmap.2023.v4i1.3596.
short: 'O.K. Aimiyekagbon, A. Löwen, A. Bender, L. Muth, W. Sextro, in: Proceedings
of the Asia Pacific Conference of the PHM Society 2023 , 2023.'
date_created: 2023-09-18T07:52:32Z
date_updated: 2024-08-19T07:39:12Z
department:
- _id: '151'
doi: 10.36001/phmap.2023.v4i1.3596
intvolume: ' 4'
issue: '1'
keyword:
- PHM
- Fault Diagnostics
- Multiple Fault Modes
- Expert-Informed Diagnostics
- Anomaly Detection
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.papers.phmsociety.org/index.php/phmap/article/view/3596
oa: '1'
publication: 'Proceedings of the Asia Pacific Conference of the PHM Society 2023 '
quality_controlled: '1'
status: public
title: Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft
Propulsion System
type: conference
user_id: '9557'
volume: 4
year: '2023'
...