---
_id: '58556'
abstract:
- lang: eng
text: 'To predict and prevent uneven tire wear in addition to a reduction of overall
tire wear, it is essential to estimate not only the total amount of wear but also
how the wear is distributed across the tire width. This requires knowledge of
the frictional power distribution in the tire contact patch, which is the basis
for calculating tire wear using a wear law. Usually, only 3D structural tire models
can generate such distributed contact results. However, they involve high computational
costs and cannot be used for comprehensive optimization of a vehicle’s suspension
system with respect to tire wear characteristics. Hence, this contribution presents
a methodology on how to accelerate the prediction of the frictional power distribution
using two components: The structural tire model is replaced by an empirical tire
model that on its own is not able to generate distributed contact results. Therefore,
an artificial neural network is trained to predict the desired contact results
from the kinematic quantities calculated by the empirical tire model. In the initial
training phase, both components are fitted to data generated by the original complex
tire model. After training, the empirical tire model can replace the structural
tire model in vehicle simulations, resulting in significantly shorter calculation
times. The simulation results are fed into the artificial neural network, which
predicts the frictional power distributions over the tire width with negligible
additional effort. Overall, the methodology reduces calculation time for the prediction
of tire wear based on virtual test drives to approximately 25% of the time needed
when using structural tire models.'
article_type: original
author:
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Raphael
full_name: Zharia, Raphael
last_name: Zharia
- first_name: Hürkan
full_name: Sahin, Hürkan
last_name: Sahin
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: Muth L, Zharia R, Sahin H, Sextro W. Prediction of the Frictional Power Distribution
in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural
Network. Tire Science and Technology. Published online 2025. doi:https://doi.org/10.2346/TST-24-009
apa: Muth, L., Zharia, R., Sahin, H., & Sextro, W. (2025). Prediction of the
Frictional Power Distribution in the Tire Contact Patch Based on an Empirical
Tire Model and an Artificial Neural Network. Tire Science and Technology.
https://doi.org/10.2346/TST-24-009
bibtex: '@article{Muth_Zharia_Sahin_Sextro_2025, title={Prediction of the Frictional
Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model
and an Artificial Neural Network}, DOI={https://doi.org/10.2346/TST-24-009},
journal={Tire Science and Technology}, publisher={The Tire Society}, author={Muth,
Lars and Zharia, Raphael and Sahin, Hürkan and Sextro, Walter}, year={2025} }'
chicago: Muth, Lars, Raphael Zharia, Hürkan Sahin, and Walter Sextro. “Prediction
of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical
Tire Model and an Artificial Neural Network.” Tire Science and Technology,
2025. https://doi.org/10.2346/TST-24-009.
ieee: 'L. Muth, R. Zharia, H. Sahin, and W. Sextro, “Prediction of the Frictional
Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model
and an Artificial Neural Network,” Tire Science and Technology, 2025, doi:
https://doi.org/10.2346/TST-24-009.'
mla: Muth, Lars, et al. “Prediction of the Frictional Power Distribution in the
Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network.”
Tire Science and Technology, The Tire Society, 2025, doi:https://doi.org/10.2346/TST-24-009.
short: L. Muth, R. Zharia, H. Sahin, W. Sextro, Tire Science and Technology (2025).
date_created: 2025-02-10T19:54:28Z
date_updated: 2025-02-27T19:53:09Z
department:
- _id: '151'
- _id: '9'
doi: https://doi.org/10.2346/TST-24-009
extern: '1'
language:
- iso: eng
publication: Tire Science and Technology
publication_status: epub_ahead
publisher: The Tire Society
quality_controlled: '1'
status: public
title: Prediction of the Frictional Power Distribution in the Tire Contact Patch Based
on an Empirical Tire Model and an Artificial Neural Network
type: journal_article
user_id: '77313'
year: '2025'
...
---
_id: '56113'
abstract:
- lang: eng
text: Abstract This study focuses on hybrid modeling approaches that combine physical
and data-driven methods to create more effective dynamical system models. In particular,
it examines discrepancy models, a type of hybrid model that integrates a physical
system model with data-driven compensation for inaccuracies. The study applies
two discrepancy modeling methods to a multibody system using discrepancies in
the state vector and its time derivative, respectively. As an application example,
a four-bar linkage with nonlinear damping is investigated, using a simplified
conservative system as a physical model. The comparative analysis of the two methods
shows that the continuous approach generally outperforms the discrete method in
terms of accuracy and computational efficiency, especially for velocity prediction
and prediction horizon. However, scenarios, where input signals for training and
testing differ, present nuanced findings. When the continuous method is trained
on complex signals (sine) and tested on simpler ones (stair), it struggles to
deliver satisfactory results, exhibiting notably higher root mean square error
(RMSE) values, particularly in angular velocity prediction. Conversely, training
on simple signals (stair) and testing on complex ones (sine) surprisingly yields
low RMSE values, indicating the continuous method’s adaptability. While the discrete
method aligns more closely with expectations and performs better in certain scenarios,
its results are consistently moderate, neither exceptional nor particularly poor.
The study also introduces a selection framework for choosing the most suitable
algorithm based on the specific characteristics of the modeling task. This framework
provides guidance for researchers and practitioners in leveraging hybrid modeling
effectively. Finally, the study concludes with an outlook on future research directions.
author:
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Benedict
full_name: Röder, Benedict
last_name: Röder
- first_name: Henrik
full_name: Ebel, Henrik
last_name: Ebel
- 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
- first_name: Peter
full_name: Eberhard, Peter
last_name: Eberhard
citation:
ama: 'Wohlleben MC, Röder B, Ebel H, Muth L, Sextro W, Eberhard P. Hybrid modeling
of multibody systems: Comparison of two discrepancy models for trajectory prediction.
PAMM. Published online 2024:e202400027. doi:https://doi.org/10.1002/pamm.202400027'
apa: 'Wohlleben, M. C., Röder, B., Ebel, H., Muth, L., Sextro, W., & Eberhard,
P. (2024). Hybrid modeling of multibody systems: Comparison of two discrepancy
models for trajectory prediction. PAMM, e202400027. https://doi.org/10.1002/pamm.202400027'
bibtex: '@article{Wohlleben_Röder_Ebel_Muth_Sextro_Eberhard_2024, title={Hybrid
modeling of multibody systems: Comparison of two discrepancy models for trajectory
prediction}, DOI={https://doi.org/10.1002/pamm.202400027},
journal={PAMM}, author={Wohlleben, Meike Claudia and Röder, Benedict and Ebel,
Henrik and Muth, Lars and Sextro, Walter and Eberhard, Peter}, year={2024}, pages={e202400027}
}'
chicago: 'Wohlleben, Meike Claudia, Benedict Röder, Henrik Ebel, Lars Muth, Walter
Sextro, and Peter Eberhard. “Hybrid Modeling of Multibody Systems: Comparison
of Two Discrepancy Models for Trajectory Prediction.” PAMM, 2024, e202400027.
https://doi.org/10.1002/pamm.202400027.'
ieee: 'M. C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, and P. Eberhard,
“Hybrid modeling of multibody systems: Comparison of two discrepancy models for
trajectory prediction,” PAMM, p. e202400027, 2024, doi: https://doi.org/10.1002/pamm.202400027.'
mla: 'Wohlleben, Meike Claudia, et al. “Hybrid Modeling of Multibody Systems: Comparison
of Two Discrepancy Models for Trajectory Prediction.” PAMM, 2024, p. e202400027,
doi:https://doi.org/10.1002/pamm.202400027.'
short: M.C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, P. Eberhard, PAMM
(2024) e202400027.
date_created: 2024-09-11T13:38:03Z
date_updated: 2025-02-27T19:54:08Z
doi: https://doi.org/10.1002/pamm.202400027
language:
- iso: eng
page: e202400027
publication: PAMM
quality_controlled: '1'
status: public
title: 'Hybrid modeling of multibody systems: Comparison of two discrepancy models
for trajectory prediction'
type: journal_article
user_id: '77313'
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: '46813'
abstract:
- lang: eng
text: Modelling of dynamic systems plays an important role in many engineering disciplines.
Two different approaches are physical modelling and data‐driven modelling, both
of which have their respective advantages and disadvantages. By combining these
two approaches, hybrid models can be created in which the respective disadvantages
are mitigated, with discrepancy models being a particular subclass. Here, the
basic system behaviour is described physically, that is, in the form of differential
equations. Inaccuracies resulting from insufficient modelling or numerics lead
to a discrepancy between the measurements and the model, which can be compensated
by a data‐driven error correction term. Since discrepancy methods still require
a large amount of measurement data, this paper investigates the extent to which
a single discrepancy model can be trained for a physical model with additional
parameter dependencies without the need for retraining. As an example, a damped
electromagnetic oscillating circuit is used. The physical model is realised by
a differential equation describing the electric current, considering only inductance
and capacitance; dissipation due to resistance is neglected. This creates a discrepancy
between measurement and model, which is corrected by a data‐driven model. In the
experiments, the inductance and the capacity are varied. It is found that the
same data‐driven model can only be used if additional parametric dependencies
in the data‐driven term are considered as well.
author:
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Wohlleben MC, Muth L, Peitz S, Sextro W. Transferability of a discrepancy
model for the dynamics of electromagnetic oscillating circuits. In: Proceedings
in Applied Mathematics and Mechanics. Wiley; 2023. doi:10.1002/pamm.202300039'
apa: Wohlleben, M. C., Muth, L., Peitz, S., & Sextro, W. (2023). Transferability
of a discrepancy model for the dynamics of electromagnetic oscillating circuits.
Proceedings in Applied Mathematics and Mechanics. https://doi.org/10.1002/pamm.202300039
bibtex: '@inproceedings{Wohlleben_Muth_Peitz_Sextro_2023, title={Transferability
of a discrepancy model for the dynamics of electromagnetic oscillating circuits},
DOI={10.1002/pamm.202300039},
booktitle={Proceedings in Applied Mathematics and Mechanics}, publisher={Wiley},
author={Wohlleben, Meike Claudia and Muth, Lars and Peitz, Sebastian and Sextro,
Walter}, year={2023} }'
chicago: Wohlleben, Meike Claudia, Lars Muth, Sebastian Peitz, and Walter Sextro.
“Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating
Circuits.” In Proceedings in Applied Mathematics and Mechanics. Wiley,
2023. https://doi.org/10.1002/pamm.202300039.
ieee: 'M. C. Wohlleben, L. Muth, S. Peitz, and W. Sextro, “Transferability of a
discrepancy model for the dynamics of electromagnetic oscillating circuits,” 2023,
doi: 10.1002/pamm.202300039.'
mla: Wohlleben, Meike Claudia, et al. “Transferability of a Discrepancy Model for
the Dynamics of Electromagnetic Oscillating Circuits.” Proceedings in Applied
Mathematics and Mechanics, Wiley, 2023, doi:10.1002/pamm.202300039.
short: 'M.C. Wohlleben, L. Muth, S. Peitz, W. Sextro, in: Proceedings in Applied
Mathematics and Mechanics, Wiley, 2023.'
date_created: 2023-09-06T05:18:05Z
date_updated: 2023-09-21T14:47:20Z
department:
- _id: '655'
- _id: '151'
doi: 10.1002/pamm.202300039
keyword:
- Electrical and Electronic Engineering
- Atomic and Molecular Physics
- and Optics
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202300039
oa: '1'
publication: Proceedings in Applied Mathematics and Mechanics
publication_identifier:
issn:
- 1617-7061
- 1617-7061
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Transferability of a discrepancy model for the dynamics of electromagnetic
oscillating circuits
type: conference
user_id: '77313'
year: '2023'
...
---
_id: '29934'
abstract:
- lang: eng
text: Tire and road wear are a major source of emissions of nonexhaust particulate
matter (PM) and make up the largest share of microplastics in the environment.
To reduce tire wear through numerical optimization of a vehicle's suspension system,
fast simulations of the representative usage of a vehicle are needed. Therefore,
this contribution evaluates if instead of a full simulation of a representative
test drive, only specific driving maneuvers resulting from a clustering of the
driving data can be used to predict tire wear. As a measure for tire wear, the
friction work between tire and road is calculated. It is shown that enough clusters
result in negligible deviations between the total friction work of the full simulation
and the cluster simulations as well as between the distributions of the friction
work over the tire width. The calculation time can be reduced to about 1% of the
full simulation.
author:
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Christian
full_name: Noll, Christian
last_name: Noll
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Muth L, Noll C, Sextro W. Generation of a Reduced, Representative, Virtual
Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data. In:
Orlova A, Cole D, eds. Advances in Dynamics of Vehicles on Roads and Tracks
II - Proceedings of the 27th Symposium of the International Association of Vehicle
System Dynamics, IAVSD 2021. Lecture Notes in Mechanical Engineering. Springer;
2022. doi:10.1007/978-3-031-07305-2_92'
apa: Muth, L., Noll, C., & Sextro, W. (2022). Generation of a Reduced, Representative,
Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data.
In A. Orlova & D. Cole (Eds.), Advances in Dynamics of Vehicles on Roads
and Tracks II - Proceedings of the 27th Symposium of the International Association
of Vehicle System Dynamics, IAVSD 2021. Springer. https://doi.org/10.1007/978-3-031-07305-2_92
bibtex: '@inproceedings{Muth_Noll_Sextro_2022, place={Cham}, series={Lecture Notes
in Mechanical Engineering}, title={Generation of a Reduced, Representative, Virtual
Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data}, DOI={10.1007/978-3-031-07305-2_92},
booktitle={Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings
of the 27th Symposium of the International Association of Vehicle System Dynamics,
IAVSD 2021}, publisher={Springer}, author={Muth, Lars and Noll, Christian and
Sextro, Walter}, editor={Orlova, Anna and Cole, David}, year={2022}, collection={Lecture
Notes in Mechanical Engineering} }'
chicago: 'Muth, Lars, Christian Noll, and Walter Sextro. “Generation of a Reduced,
Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering
of Driving Data.” In Advances in Dynamics of Vehicles on Roads and Tracks II
- Proceedings of the 27th Symposium of the International Association of Vehicle
System Dynamics, IAVSD 2021, edited by Anna Orlova and David Cole. Lecture
Notes in Mechanical Engineering. Cham: Springer, 2022. https://doi.org/10.1007/978-3-031-07305-2_92.'
ieee: 'L. Muth, C. Noll, and W. Sextro, “Generation of a Reduced, Representative,
Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data,”
in Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of
the 27th Symposium of the International Association of Vehicle System Dynamics,
IAVSD 2021, Saint Petersburg, Russia, 2022, doi: 10.1007/978-3-031-07305-2_92.'
mla: Muth, Lars, et al. “Generation of a Reduced, Representative, Virtual Test Drive
for Fast Evaluation of Tire Wear by Clustering of Driving Data.” Advances in
Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium
of the International Association of Vehicle System Dynamics, IAVSD 2021, edited
by Anna Orlova and David Cole, Springer, 2022, doi:10.1007/978-3-031-07305-2_92.
short: 'L. Muth, C. Noll, W. Sextro, in: A. Orlova, D. Cole (Eds.), Advances in
Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium
of the International Association of Vehicle System Dynamics, IAVSD 2021, Springer,
Cham, 2022.'
conference:
end_date: 2021-08-19
location: Saint Petersburg, Russia
name: 27th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks, IAVSD 2021
start_date: 2021-08-17
date_created: 2022-02-21T14:14:11Z
date_updated: 2022-08-23T11:55:07Z
department:
- _id: '151'
doi: 10.1007/978-3-031-07305-2_92
editor:
- first_name: Anna
full_name: Orlova, Anna
last_name: Orlova
- first_name: David
full_name: Cole, David
last_name: Cole
keyword:
- Tire Wear
- Vehicle Dynamics
- Clustering
- Virtual Test
language:
- iso: eng
main_file_link:
- url: https://link.springer.com/chapter/10.1007/978-3-031-07305-2_92
place: Cham
publication: Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings
of the 27th Symposium of the International Association of Vehicle System Dynamics,
IAVSD 2021
publication_identifier:
eisbn:
- 978-3-031-07305-2
isbn:
- 978-3-031-07304-5
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Mechanical Engineering
status: public
title: Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation
of Tire Wear by Clustering of Driving Data
type: conference
user_id: '77313'
year: '2022'
...
---
_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'
...