---
_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: '51518'
abstract:
- lang: eng
text: In applications of piezoelectric actuators and sensors, the dependability
and particularly the reliability throughout their lifetime are vital to manufacturers
and end-users and are enabled through condition-monitoring approaches. Existing
approaches often utilize impedance measurements over a range of frequencies or
velocity measurements and require additional equipment or sensors, such as a laser
Doppler vibrometer. Furthermore, the non-negligible effects of varying operating
conditions are often unconsidered. To minimize the need for additional sensors
while maintaining the dependability of piezoelectric bending actuators irrespective
of varying operating conditions, an online diagnostics approach is proposed. To
this end, time- and frequency-domain features are extracted from monitored current
signals to reflect hairline crack development in bending actuators. For validation
of applicability, the presented analysis method was evaluated on piezoelectric
bending actuators subjected to accelerated lifetime tests at varying voltage amplitudes
and under external damping conditions. In the presence of a crack and due to a
diminished stiffness, the resonance frequency decreases and the root-mean-square
amplitude of the current signal simultaneously abruptly drops during the lifetime
tests. Furthermore, the piezoelectric crack surfaces clapping is reflected in
higher harmonics of the current signal. Thus, time-domain features and harmonics
of the current signals are sufficient to diagnose hairline cracks in the actuators.
article_number: '521'
article_type: original
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Tobias
full_name: Hemsel, Tobias
id: '210'
last_name: Hemsel
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: Aimiyekagbon OK, Bender A, Hemsel T, Sextro W. Diagnostics of Piezoelectric
Bending Actuators Subjected to Varying Operating Conditions. Electronics.
2024;13(3). doi:10.3390/electronics13030521
apa: Aimiyekagbon, O. K., Bender, A., Hemsel, T., & Sextro, W. (2024). Diagnostics
of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions.
Electronics, 13(3), Article 521. https://doi.org/10.3390/electronics13030521
bibtex: '@article{Aimiyekagbon_Bender_Hemsel_Sextro_2024, title={Diagnostics of
Piezoelectric Bending Actuators Subjected to Varying Operating Conditions}, volume={13},
DOI={10.3390/electronics13030521},
number={3521}, journal={Electronics}, publisher={MDPI AG}, author={Aimiyekagbon,
Osarenren Kennedy and Bender, Amelie and Hemsel, Tobias and Sextro, Walter}, year={2024}
}'
chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, Tobias Hemsel, and Walter
Sextro. “Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating
Conditions.” Electronics 13, no. 3 (2024). https://doi.org/10.3390/electronics13030521.
ieee: 'O. K. Aimiyekagbon, A. Bender, T. Hemsel, and W. Sextro, “Diagnostics of
Piezoelectric Bending Actuators Subjected to Varying Operating Conditions,” Electronics,
vol. 13, no. 3, Art. no. 521, 2024, doi: 10.3390/electronics13030521.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Diagnostics of Piezoelectric Bending
Actuators Subjected to Varying Operating Conditions.” Electronics, vol.
13, no. 3, 521, MDPI AG, 2024, doi:10.3390/electronics13030521.
short: O.K. Aimiyekagbon, A. Bender, T. Hemsel, W. Sextro, Electronics 13 (2024).
date_created: 2024-02-20T06:46:43Z
date_updated: 2024-03-15T16:15:56Z
department:
- _id: '151'
doi: 10.3390/electronics13030521
funded_apc: '1'
intvolume: ' 13'
issue: '3'
keyword:
- piezoelectric transducer
- self-sensing
- fault detection
- diagnostics
- hairline crack
- condition monitoring
language:
- iso: eng
publication: Electronics
publication_identifier:
issn:
- 2079-9292
publication_status: published
publisher: MDPI AG
quality_controlled: '1'
status: public
title: Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating
Conditions
type: journal_article
user_id: '9557'
volume: 13
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'
...
---
_id: '44672'
abstract:
- lang: eng
text: With enhancing digitalization, condition monitoring is used in an increasing
number of application fields across various industrial sectors. By its application,
increased reliability as well as reduced risks and costs can be achieved. Based
on different approaches, technical systems are monitored and measured data is
analyzed to enable condition-based or predictive maintenance. To this end, machine
learning approaches are usually implemented to diagnose the health states or predict
the health index of the monitored system. However, these trained models are often
black-box models, not intuitively explainable for a human. To overcome this shortcoming,
a model-based approach based on physics is developed for piezoelectric bending
actuators. Such a model enables a transparent representation of the system. Moreover,
the model-based approach is extended by a parameter-estimation to account for
sudden changes in behavior e. g. caused by occurring cracks.
article_number: '114399'
article_type: original
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
citation:
ama: 'Bender A. Model-based condition monitoring of piezoelectric bending actuators.
Sensors and Actuators A: Physical. 2023;357. doi:10.1016/j.sna.2023.114399'
apa: 'Bender, A. (2023). Model-based condition monitoring of piezoelectric bending
actuators. Sensors and Actuators A: Physical, 357, Article 114399.
https://doi.org/10.1016/j.sna.2023.114399'
bibtex: '@article{Bender_2023, title={Model-based condition monitoring of piezoelectric
bending actuators}, volume={357}, DOI={10.1016/j.sna.2023.114399},
number={114399}, journal={Sensors and Actuators A: Physical}, publisher={Elsevier
BV}, author={Bender, Amelie}, year={2023} }'
chicago: 'Bender, Amelie. “Model-Based Condition Monitoring of Piezoelectric Bending
Actuators.” Sensors and Actuators A: Physical 357 (2023). https://doi.org/10.1016/j.sna.2023.114399.'
ieee: 'A. Bender, “Model-based condition monitoring of piezoelectric bending actuators,”
Sensors and Actuators A: Physical, vol. 357, Art. no. 114399, 2023, doi:
10.1016/j.sna.2023.114399.'
mla: 'Bender, Amelie. “Model-Based Condition Monitoring of Piezoelectric Bending
Actuators.” Sensors and Actuators A: Physical, vol. 357, 114399, Elsevier
BV, 2023, doi:10.1016/j.sna.2023.114399.'
short: 'A. Bender, Sensors and Actuators A: Physical 357 (2023).'
date_created: 2023-05-09T09:49:44Z
date_updated: 2023-05-09T09:53:31Z
department:
- _id: '151'
doi: 10.1016/j.sna.2023.114399
intvolume: ' 357'
keyword:
- Condition Monitoring
- Model-based approach Diagnostics
- Varying conditions
- Explainability
- Piezoelectric bending actuators
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://authors.elsevier.com/a/1h2WV3IC9dF7Hm
oa: '1'
publication: 'Sensors and Actuators A: Physical'
publication_identifier:
issn:
- 0924-4247
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: Model-based condition monitoring of piezoelectric bending actuators
type: journal_article
user_id: '54290'
volume: 357
year: '2023'
...
---
_id: '22507'
abstract:
- lang: eng
text: Several methods, including order analysis, wavelet analysis and empirical
mode decomposition have been proposed and successfully employed for the health
state estimation of technical systems operating under varying conditions. However,
where information such as the speed of rotating machinery, component specifications
or other domain-specific information is unavailable, such methods are often infeasible.
Thus, this paper investigates the application of classical time-domain features,
features from the medical field and novel features from the highly comparative
time-series analysis (HCTSA) package, for the health state estimation of rotating
machinery operating under varying conditions. Furthermore, several feature selection
methods are investigated to identify features as viable health indicators for
the diagnostics and prognostics of technical systems. As a case study, the presented
methods are evaluated on real-world and experimentally acquired vibration data
of bearings operating under varying speed. The results show that the selected
features can successfully be employed as health indicators for technical systems
operating under varying conditions.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- 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: 'Aimiyekagbon OK, Bender A, Sextro W. On the applicability of time series features
as health indicators for technical systems operating under varying conditions.
In: Proceedings of the Seventeenth International Conference on Condition Monitoring
and Asset Management (CM 2021).'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (n.d.). On the applicability
of time series features as health indicators for technical systems operating under
varying conditions. Proceedings of the Seventeenth International Conference
on Condition Monitoring and Asset Management (CM 2021). Seventeenth International
Conference on Condition Monitoring and Asset Management (CM 2021).
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro, title={On the applicability
of time series features as health indicators for technical systems operating under
varying conditions}, booktitle={Proceedings of the Seventeenth International Conference
on Condition Monitoring and Asset Management (CM 2021)}, author={Aimiyekagbon,
Osarenren Kennedy and Bender, Amelie and Sextro, Walter} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “On
the Applicability of Time Series Features as Health Indicators for Technical Systems
Operating under Varying Conditions.” In Proceedings of the Seventeenth International
Conference on Condition Monitoring and Asset Management (CM 2021), n.d.
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “On the applicability of time
series features as health indicators for technical systems operating under varying
conditions,” presented at the Seventeenth International Conference on Condition
Monitoring and Asset Management (CM 2021).
mla: Aimiyekagbon, Osarenren Kennedy, et al. “On the Applicability of Time Series
Features as Health Indicators for Technical Systems Operating under Varying Conditions.”
Proceedings of the Seventeenth International Conference on Condition Monitoring
and Asset Management (CM 2021).
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: Proceedings of the Seventeenth
International Conference on Condition Monitoring and Asset Management (CM 2021),
n.d.'
conference:
end_date: 2021-06-18
name: Seventeenth International Conference on Condition Monitoring and Asset Management
(CM 2021)
start_date: 2021-06-14
date_created: 2021-06-23T05:24:39Z
date_updated: 2023-09-22T08:10:34Z
ddc:
- '620'
department:
- _id: '151'
file:
- access_level: open_access
content_type: application/pdf
creator: kennedy
date_created: 2021-06-23T06:43:44Z
date_updated: 2021-06-23T06:50:07Z
description: 'This is a post-print version of the article presented at the Seventeenth
International Con-ference on Condition Monitoring and Asset Management (CM 2021).
The event websiteis available at: https://www.bindt.org/events/CM-2021/ and the
abstract is available at:https://www.bindt.org/events/CM-2021/abstract-9a7/.'
file_id: '22508'
file_name: Aimiyekagbon_et_al_2021_On_the_applicability_of_time_series_features_as_health_indicators_postPrint.pdf
file_size: 1875572
relation: main_file
title: On the applicability of time series features as health indicators for technical
systems operating under varying conditions
file_date_updated: 2021-06-23T06:50:07Z
has_accepted_license: '1'
keyword:
- Wind turbine diagnostics
- bearing diagnostics
- non-stationary operating conditions
- varying operating conditions
- feature extraction
- feature selection
- fault detection
- failure detection
language:
- iso: eng
oa: '1'
publication: Proceedings of the Seventeenth International Conference on Condition
Monitoring and Asset Management (CM 2021)
publication_status: inpress
quality_controlled: '1'
status: public
title: On the applicability of time series features as health indicators for technical
systems operating under varying conditions
type: conference
user_id: '9557'
year: '2021'
...
---
_id: '27111'
abstract:
- lang: eng
text: In the industry 4.0 era, there is a growing need to transform unstructured
data acquired by a multitude of sources into information and subsequently into
knowledge to improve the quality of manufactured products, to boost production,
for predictive maintenance, etc. Data-driven approaches, such as machine learning
techniques, are typically employed to model the underlying relationship from data.
However, an increase in model accuracy with state-of-the-art methods, such as
deep convolutional neural networks, results in less interpretability and transparency.
Due to the ease of implementation, interpretation and transparency to both domain
experts and non-experts, a rule-based method is proposed in this paper, for prognostics
and health management (PHM) and specifically for diagnostics. The proposed method
utilizes the most relevant sensor signals acquired via feature extraction and
selection techniques and expert knowledge. As a case study, the presented method
is evaluated on data from a real-world quality control set-up provided by the
European prognostics and health management society (PHME) at the conference’s
2021 data challenge. With the proposed method, our team took the third place,
capable of successfully diagnosing different fault modes, irrespective of varying
conditions.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- 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: 'Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics
of a Production Line. In: Do P, King S, Fink O, eds. Proceedings of the European
Conference of the PHM Society 2021. Vol 6. ; 2021:527-536. doi:10.36001/phme.2021.v6i1.3042'
apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., & Sextro,
W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &
O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021
(Vol. 6, Issue 1, pp. 527–536). https://doi.org/10.36001/phme.2021.v6i1.3042
bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based
Diagnostics of a Production Line}, volume={6}, DOI={10.36001/phme.2021.v6i1.3042},
number={1}, booktitle={Proceedings of the European Conference of the PHM Society
2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike
Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve
and Fink, Olga}, year={2021}, pages={527–536} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie
Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In Proceedings
of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve
King, and Olga Fink, 6:527–36, 2021. https://doi.org/10.36001/phme.2021.v6i1.3042.
ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based
Diagnostics of a Production Line,” in Proceedings of the European Conference
of the PHM Society 2021, 2021, vol. 6, no. 1, pp. 527–536, doi: 10.36001/phme.2021.v6i1.3042.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production
Line.” Proceedings of the European Conference of the PHM Society 2021,
edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:10.36001/phme.2021.v6i1.3042.
short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P.
Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM
Society 2021, 2021, pp. 527–536.'
conference:
name: PHM Society European Conference
date_created: 2021-11-03T12:26:39Z
date_updated: 2023-09-22T09:13:01Z
department:
- _id: '151'
doi: 10.36001/phme.2021.v6i1.3042
editor:
- first_name: Phuc
full_name: Do, Phuc
last_name: Do
- first_name: Steve
full_name: King, Steve
last_name: King
- first_name: Olga
full_name: Fink, Olga
last_name: Fink
intvolume: ' 6'
issue: '1'
keyword:
- PHME 2021
- Feature Selection Classification
- Feature Selection Clustering
- Interpretable Model
- Transparent Model
- Industry 4.0
- Real-World Diagnostics
- Quality Control
- Predictive Maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812
oa: '1'
page: 527-536
publication: Proceedings of the European Conference of the PHM Society 2021
publication_status: published
quality_controlled: '1'
status: public
title: Rule-based Diagnostics of a Production Line
type: conference
user_id: '9557'
volume: 6
year: '2021'
...