---
_id: '50649'
abstract:
- lang: eng
text: The energy turnaround and the shift towards sustainable mobility threaten
the stability of European energy distribution grids due to substantially increasing
load fluctuations and power demand. These challenges can critically impact assets
in the distribution grid—e.g., switchgears—intensifying the need to plan, conduct,
and manage the maintenance of such assets. Predictive maintenance strategies that
analyze assets' current and historical condition data have been discussed as promising
approaches toward that end. However, the extant research focuses on designing
and improving analytical algorithms or information technology (IT) artifacts while
not considering how a maintenance service is cocreated by companies with IT. This
research article posits that IT and service must be aligned closely, presenting
an ensemble artifact comprising a digital industrial platform and a smart service
system for predictive maintenance on the distribution grid. The artifact is evaluated
by conducting a willingness-to-pay analysis with asset operators, documenting
their demand for condition monitoring and predictive maintenance as an integrated
solution, although they still struggle with even getting the condition data of
their assets. Building on these results, we formalize the knowledge in the form
of design principles and implications for managing the maintenance of critical
assets in the distribution grid.
article_type: original
author:
- first_name: Philipp
full_name: zur Heiden, Philipp
id: '64394'
last_name: zur Heiden
- first_name: Jennifer
full_name: Priefer, Jennifer
id: '82872'
last_name: Priefer
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
citation:
ama: zur Heiden P, Priefer J, Beverungen D. Predictive Maintenance on the Energy
Distribution Grid—Design and Evaluation of a Digital Industrial Platform in the
Context of a Smart Service System. IEEE Transactions on Engineering Management.
2024;71:3641-3655. doi:10.1109/tem.2024.3352819
apa: zur Heiden, P., Priefer, J., & Beverungen, D. (2024). Predictive Maintenance
on the Energy Distribution Grid—Design and Evaluation of a Digital Industrial
Platform in the Context of a Smart Service System. IEEE Transactions on Engineering
Management, 71, 3641–3655. https://doi.org/10.1109/tem.2024.3352819
bibtex: '@article{zur Heiden_Priefer_Beverungen_2024, title={Predictive Maintenance
on the Energy Distribution Grid—Design and Evaluation of a Digital Industrial
Platform in the Context of a Smart Service System}, volume={71}, DOI={10.1109/tem.2024.3352819},
journal={IEEE Transactions on Engineering Management}, publisher={Institute of
Electrical and Electronics Engineers (IEEE)}, author={zur Heiden, Philipp and
Priefer, Jennifer and Beverungen, Daniel}, year={2024}, pages={3641–3655} }'
chicago: 'Heiden, Philipp zur, Jennifer Priefer, and Daniel Beverungen. “Predictive
Maintenance on the Energy Distribution Grid—Design and Evaluation of a Digital
Industrial Platform in the Context of a Smart Service System.” IEEE Transactions
on Engineering Management 71 (2024): 3641–55. https://doi.org/10.1109/tem.2024.3352819.'
ieee: 'P. zur Heiden, J. Priefer, and D. Beverungen, “Predictive Maintenance on
the Energy Distribution Grid—Design and Evaluation of a Digital Industrial Platform
in the Context of a Smart Service System,” IEEE Transactions on Engineering
Management, vol. 71, pp. 3641–3655, 2024, doi: 10.1109/tem.2024.3352819.'
mla: zur Heiden, Philipp, et al. “Predictive Maintenance on the Energy Distribution
Grid—Design and Evaluation of a Digital Industrial Platform in the Context of
a Smart Service System.” IEEE Transactions on Engineering Management, vol.
71, Institute of Electrical and Electronics Engineers (IEEE), 2024, pp. 3641–55,
doi:10.1109/tem.2024.3352819.
short: P. zur Heiden, J. Priefer, D. Beverungen, IEEE Transactions on Engineering
Management 71 (2024) 3641–3655.
date_created: 2024-01-19T11:47:56Z
date_updated: 2024-11-11T09:50:34Z
ddc:
- '620'
doi: 10.1109/tem.2024.3352819
file:
- access_level: closed
content_type: application/pdf
creator: dabe
date_created: 2024-04-18T12:26:27Z
date_updated: 2024-04-18T12:26:27Z
file_id: '53570'
file_name: Predictive_Maintenance_on_the_Energy_Distribution_GridDesign_and_Evaluation_of_a_Digital_Industrial_Platform_in_the_Context_of_a_Smart_Service_System.pdf
file_size: 4070804
relation: main_file
success: 1
file_date_updated: 2024-04-18T12:26:27Z
has_accepted_license: '1'
intvolume: ' 71'
keyword:
- Design science research
- digital platform
- distribution grid
- IS design
- predictive maintenance
- smart services
language:
- iso: eng
page: 3641-3655
project:
- _id: '651'
grant_number: 03E16012F
name: 'FLEMING: FLEMING - Flexible Monitoring- und Regelsysteme für die Energie-
und Mobilitätswende im Verteilnetz durch Einsatz von Künstlicher Intelligenz'
publication: IEEE Transactions on Engineering Management
publication_identifier:
issn:
- 0018-9391
- 1558-0040
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
quality_controlled: '1'
status: public
title: Predictive Maintenance on the Energy Distribution Grid—Design and Evaluation
of a Digital Industrial Platform in the Context of a Smart Service System
type: journal_article
user_id: '82872'
volume: 71
year: '2024'
...
---
_id: '25046'
abstract:
- lang: eng
text: While increasing digitalization enables multiple advantages for a
reliable operation of technical systems, a remaining challenge in the context
of condition monitoring is seen in suitable consideration of uncertainties affecting
the monitored system. Therefore, a suitable prognostic approach to predict the
remaining useful lifetime of complex technical systems is required. To handle
different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based
prognostic approach is developed and evaluated by the use case of rubber-metal-elements.
These elements are maintained preventively due to the strong influence of uncertainties
on their behavior. In this paper, two measurement quantities are compared concerning
their ability to establish a prediction of the remaining useful lifetime of the
monitored elements and the influence of present uncertainties. Based on three
performance indices, the results are evaluated. A comparison with predictions
of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle
Filter. Finally, the value of the developed method for enabling condition monitoring
of technical systems related to uncertainties is given exemplary by a comparison
between the preventive and the predictive maintenance strategy for the use case.
article_number: '210'
article_type: original
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
citation:
ama: Bender A. A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider
Uncertainties in RUL Predictions. Machines. 2021;9(10). doi:10.3390/machines9100210
apa: Bender, A. (2021). A Multi-Model-Particle Filtering-Based Prognostic Approach
to Consider Uncertainties in RUL Predictions. Machines, 9(10), Article
210. https://doi.org/10.3390/machines9100210
bibtex: '@article{Bender_2021, title={A Multi-Model-Particle Filtering-Based Prognostic
Approach to Consider Uncertainties in RUL Predictions}, volume={9}, DOI={10.3390/machines9100210},
number={10210}, journal={Machines}, author={Bender, Amelie}, year={2021} }'
chicago: Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach
to Consider Uncertainties in RUL Predictions.” Machines 9, no. 10 (2021).
https://doi.org/10.3390/machines9100210.
ieee: 'A. Bender, “A Multi-Model-Particle Filtering-Based Prognostic Approach to
Consider Uncertainties in RUL Predictions,” Machines, vol. 9, no. 10, Art.
no. 210, 2021, doi: 10.3390/machines9100210.'
mla: Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach
to Consider Uncertainties in RUL Predictions.” Machines, vol. 9, no. 10,
210, 2021, doi:10.3390/machines9100210.
short: A. Bender, Machines 9 (2021).
date_created: 2021-09-27T07:07:58Z
date_updated: 2022-11-03T11:42:46Z
department:
- _id: '151'
doi: 10.3390/machines9100210
intvolume: ' 9'
issue: '10'
keyword:
- prognostics
- RUL predictions
- particle filter
- uncertainty consideration
- Multi-Model-Particle Filter
- model-based approach
- rubber-metal-elements
- predictive maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.mdpi.com/2075-1702/9/10/210
oa: '1'
publication: Machines
publication_identifier:
issn:
- 2075-1702
publication_status: published
quality_controlled: '1'
status: public
title: A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties
in RUL Predictions
type: journal_article
user_id: '54290'
volume: 9
year: '2021'
...
---
_id: '21630'
abstract:
- lang: ger
text: Eine zustandsbasierte Instandhaltungsstrategie reduziert das Risiko eines
Ausfalls eines technischen Systems bei gleichzeitig hoher Ausnutzung und planbaren
Instandhaltungsmaßnahmen. Das Ziel dieser Arbeit liegt in der Entwicklung einer
Zustandsüberwachung für Gummi-Metall-Elemente. Die Herausforderungen dieser Zustandsüberwachung
leiten sich aus dem viskoelastischen Verhalten sowie dem komplexen Degradationsverhalten
der Elemente ab. Infolge der daraus resultierenden Unsicherheiten werden die Elemente
heutzutage präventiv instandgehalten. In Lebensdauerversuchen der Gummi-Metall-Elemente
werden drei Messgrößen detektiert. Dabei wird mit der Temperatur eine Messgröße
identifiziert, die am geeignetsten zur Beschreibung des Zustands der Elemente
ist. Generell wird die Genauigkeit einer Zustandsüberwachung durch verschiedene
Unsicherheiten beeinflusst. Für die Prognose der nutzbaren Restlebensdauer der
Gummi-Metall-Elemente wird das Partikelfilter, eine verbreitete modellbasierte
Methode zur Zustandsüberwachung technischer Systeme, weiterentwickelt, um Unsicherheiten
im Verhalten und der Degradation der Elemente zu berücksichtigen. Anhand der Ergebnisse
wird belegt, dass aufbauend auf dieser Zustandsüberwachung die Ausnutzung der
Gummi-Metall-Elemente in realen Anwendungen durch eine präventive Instandhaltung
erhöht werden kann. Damit bildet diese Arbeit die Basis für zukünftige, prädiktive
Instandhaltungskonzepte für diese Elemente. Weiterhin bestätigt die Arbeit, dass
eine Berücksichtigung vorliegender Unsicherheiten zu einem frühen Zeitpunkt im
Entwicklungsprozess des Zustandsüberwachungssystems empfehlenswert ist.
- lang: eng
text: With condition-based maintenance, the risk of system failure is reduced while
maximizing system utilization and ensuring a predictable maintenance schedule.
The aim of this thesis is the development of a condition monitoring system of
rubber-metal-elements. Due to the viscoelasticity and the complex degradation
behavior of the elements, they are currently maintained preventively. During lifetime
tests of the rubber-metal-elements, three measurement quantities are acquired.
Thereby, temperature is identified as the most suitable measurement quantity to
describe the rubber-metal-elements state. The accuracy of a condition monitoring
system is generally influenced by various uncertainties. To consider available
uncertainties within the behavior and the degradation of the elements, the particle
filter, a state-of-the-art model-based method for predicting remaining useful
life, is enhanced and employed. The results prove that the developed condition
monitoring system enables an increased utilization of rubber-metal-elements in
real applications through predictive maintenance. This thesis provides the basis
for future concepts of predictive maintenance for rubber-metal-elements. Moreover,
it is recommended to take uncertainties into account at an early stage of the
development process of condition monitoring systems.
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
citation:
ama: Bender A. Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall-Elementen
unter Berücksichtigung systembasierter Unsicherheiten. Shaker; 2021. doi:10.17619/UNIPB/1-1084
apa: Bender, A. (2021). Zustandsüberwachung zur Prognose der Restlebensdauer
von Gummi-Metall-Elementen unter Berücksichtigung systembasierter Unsicherheiten.
Shaker. https://doi.org/10.17619/UNIPB/1-1084
bibtex: '@book{Bender_2021, title={Zustandsüberwachung zur Prognose der Restlebensdauer
von Gummi-Metall-Elementen unter Berücksichtigung systembasierter Unsicherheiten},
DOI={10.17619/UNIPB/1-1084},
publisher={Shaker}, author={Bender, Amelie}, year={2021} }'
chicago: Bender, Amelie. Zustandsüberwachung zur Prognose der Restlebensdauer
von Gummi-Metall-Elementen unter Berücksichtigung systembasierter Unsicherheiten.
Shaker, 2021. https://doi.org/10.17619/UNIPB/1-1084.
ieee: A. Bender, Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall-Elementen
unter Berücksichtigung systembasierter Unsicherheiten. Shaker, 2021.
mla: Bender, Amelie. Zustandsüberwachung zur Prognose der Restlebensdauer von
Gummi-Metall-Elementen unter Berücksichtigung systembasierter Unsicherheiten.
Shaker, 2021, doi:10.17619/UNIPB/1-1084.
short: A. Bender, Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall-Elementen
unter Berücksichtigung systembasierter Unsicherheiten, Shaker, 2021.
date_created: 2021-04-15T10:29:53Z
date_updated: 2023-09-15T12:22:50Z
department:
- _id: '151'
doi: 10.17619/UNIPB/1-1084
keyword:
- Zustandsüberwachung
- Prognose der Restlebensdauer
- modellbasierte Prognose
- Partikelfilter
- Unsicherheiten
- Gummi
- Verlässlichkeit
- Lebensdauerversuche
- Predictive Maintenance
language:
- iso: ger
publisher: Shaker
status: public
supervisor:
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
- first_name: Stefan
full_name: Bracke, Stefan
last_name: Bracke
title: Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall-Elementen
unter Berücksichtigung systembasierter Unsicherheiten
type: dissertation
user_id: '210'
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'
...