---
_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
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: 2023-09-21T14:51:27Z
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: '77313'
volume: 4
year: '2023'
...
---
_id: '47159'
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.
Condition Monitor. 2022:5-10.
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2022). On the applicability
of time series features as health indicators for technical systems operating under
varying conditions. Condition Monitor, 425, 5–10.
bibtex: '@article{Aimiyekagbon_Bender_Sextro_2022, title={On the applicability of
time series features as health indicators for technical systems operating under
varying conditions}, number={425}, journal={ Condition Monitor}, author={Aimiyekagbon,
Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2022}, pages={5–10}
}'
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.” Condition Monitor, 2022.
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,” Condition Monitor, no. 425, pp. 5–10, 2022.
mla: Aimiyekagbon, Osarenren Kennedy, et al. “On the Applicability of Time Series
Features as Health Indicators for Technical Systems Operating under Varying Conditions.”
Condition Monitor, no. 425, 2022, pp. 5–10.
short: O.K. Aimiyekagbon, A. Bender, W. Sextro, Condition Monitor (2022) 5–10.
date_created: 2023-09-22T09:25:48Z
date_updated: 2023-09-22T09:29:51Z
department:
- _id: '151'
issue: '425'
language:
- iso: eng
page: 5 - 10
publication: ' Condition Monitor'
publication_date: 2022-08
publication_identifier:
issn:
- 0268-8050
publication_status: published
status: public
title: On the applicability of time series features as health indicators for technical
systems operating under varying conditions
type: newspaper_article
user_id: '9557'
year: '2022'
...
---
_id: '27652'
abstract:
- lang: ger
text: "Aufgrund der Fortschritte der Digitalisierung finden Systeme zur Zustandsüberwachung
vermehrt Einsatz in der Industrie, um durch eine zustandsbasierte oder eine prädiktive
Instandhaltung Vorteile, wie eine verbesserte Zuverlässigkeit und geringere Kosten
zu erzielen. Dabei beruhen Zustandsüberwachungssysteme auf den folgenden Bausteinen:
Sensorik, Datenvorverarbeitung, Merkmalsextraktion und -auswahl, Diagnose bzw.
Prognose sowie einer Entscheidungsfindung basierend auf den Ergebnissen. Jeder
dieser Bausteine erfordert individuelle Einstellungen, um ein geeignetes Zustandsüberwachungssystem
für die jeweilige Anwendung zu entwickeln. Eine offene Fragestellung im Bereich
der Zustandsüberwachung ergibt sich aufgrund der Unsicherheit der Zukunft, die
sich in den zukünftigen Betriebs- und Umgebungsbedingungen zeigt. Diese Unsicherheit
gilt es in allen Bausteinen zu berücksichtigen.\r\nDieser Beitrag konzentriert
sich auf den Baustein Merkmalsextraktion und -selektion, mit dem Ziel anhand geeigneter
Merkmale eine Prognose der nutzbaren Restlebensdauer mit hoher Genauigkeit realisieren
zu können. Daher werden geeignete Merkmale aus dem Zeitbereich und daraus abgeleitete
Zustandsindikatoren für die Restlebensdauerprognose von technischen Systemen vorgestellt.
Dabei sind Zustandsindikatoren Kenngrößen zur Beobachtung des Zustands der kritischen
Systemkomponenten. Anhand dreier Anwendungsbeispiele wird ihre Eignung evaluiert.
Dabei werden Daten aus Lebensdauerversuchen unter instationären Betriebs- und
Umgebungsbedingungen ausgewertet. Die auftretenden Unsicherheiten der Zukunft
werden somit berücksichtigt. Die Beispielsysteme beruhen auf Gummi-Metall-Elementen
und Wälzlagern. Aus den generierten Ergebnissen lässt sich schließen, dass die
Zustandsindikatoren aus der betrachteten Zeitreihen-Toolbox auch unter unbekannten
Betriebs- und Umgebungsbedingungen robust sind.\r\n"
- lang: eng
text: "Due to the advances in digitalization, condition monitoring systems have
found numerous applications in the industry due to benefits such as improved reliability
and lowered costs through condition-based or predictive maintenance. Condition
monitoring systems typically involve elements, such as data acquisition via suitable
sensors, data preprocessing, feature extraction and selection, diagnostics, prognostics
and (maintenance) decisions based on diagnosis or prognosis. For the application-specific
development of a suitable condition monitoring system, each of these elements
requires individual settings. Due to the uncertainty of the future, an open question
arises in the condition monitoring field, which is reflected in unknown future
operating and environmental conditions. This uncertainty needs consideration in
all elements of a condition monitoring system.\r\nThis article focuses on feature
extraction and selection, building on the hypothesis that the remaining useful
life of a technical system can be predicted with high accuracy utilizing suitable
features. In this article, health indicators derived from time-domain features
that permit the monitoring of the health of critical system components are presented
for predicting the remaining useful life of technical systems. Three distinct
application examples based on rubber-metal elements and rolling-element bearings
are evaluated to validate the suitability of the presented methods. Experimental
data from accelerated lifetime tests conducted under non-stationary operating
and environmental conditions are considered to take possible future uncertainties
into account. It can be concluded from the acquired results that health indicators
derived from the presented time series toolbox are robust to varying operating
and environmental conditions.\r\n"
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. Extraktion und Selektion geeigneter Merkmale
für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten
. In: VDI-Berichte 2391. VDI Verlag GmbH; 2021:197-210.'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2021). Extraktion und Selektion
geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz
aleatorischen Unsicherheiten . VDI-Berichte 2391, 197–210.
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2021, place={Düsseldorf}, title={Extraktion
und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen
Systemen trotz aleatorischen Unsicherheiten }, booktitle={VDI-Berichte 2391},
publisher={VDI Verlag GmbH}, author={Aimiyekagbon, Osarenren Kennedy and Bender,
Amelie and Sextro, Walter}, year={2021}, pages={197–210} }'
chicago: 'Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Extraktion
und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen
Systemen trotz aleatorischen Unsicherheiten .” In VDI-Berichte 2391, 197–210.
Düsseldorf: VDI Verlag GmbH, 2021.'
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Extraktion und Selektion geeigneter
Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen
Unsicherheiten ,” in VDI-Berichte 2391, Würzburg, 2021, pp. 197–210.
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Extraktion und Selektion geeigneter
Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen
Unsicherheiten .” VDI-Berichte 2391, VDI Verlag GmbH, 2021, pp. 197–210.
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: VDI-Berichte 2391, VDI Verlag
GmbH, Düsseldorf, 2021, pp. 197–210.'
conference:
end_date: 2021-11-17
location: Würzburg
name: '3. VDI-Fachtagung '
start_date: 2021-11-16
date_created: 2021-11-22T07:42:44Z
date_updated: 2022-01-06T06:57:43Z
department:
- _id: '151'
keyword:
- run-to-failure
- rubber-metal element
- bearing prognostics
- non-stationary operating conditions
- varying operating conditions
- feature extraction
- feature selection
language:
- iso: ger
page: 197 - 210
place: Düsseldorf
publication: VDI-Berichte 2391
publication_identifier:
isbn:
- 978-3-18-092391-8
issn:
- '0083-5560 '
publication_status: published
publisher: VDI Verlag GmbH
status: public
title: 'Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose
von technischen Systemen trotz aleatorischen Unsicherheiten '
type: conference
user_id: '9557'
year: '2021'
...
---
_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'
...
---
_id: '17810'
abstract:
- lang: eng
text: In all fields, the significance of a reliable and accurate predictive model
is almost unquantifiable. With deep domain knowledge, models derived from first
principles typically outperforms other models in terms of reliability and accuracy.
When it may become a cumbersome or an unachievable task to build or validate such
models of complex (non-linear) systems, machine learning techniques are employed
to build predictive models. However, the accuracy of such techniques is not only
dependent on the hyper-parameters of the chosen algorithm, but also on the amount
and quality of data. This paper investigates the application of classical time
series forecasting approaches for the reliable prognostics of technical systems,
where black box machine learning techniques might not successfully be employed
given insufficient amount of data and where first principles models are infeasible
due to lack of domain specific data. Forecasting by analogy, forecasting by analytical
function fitting, an exponential smoothing forecasting method and the long short-term
memory (LSTM) are evaluated and compared against the ground truth data. As a case
study, the methods are applied to predict future crack lengths of riveted aluminium
plates under cyclic loading. The performance of the predictive models is evaluated
based on error metrics leading to a proposal of when to apply which forecasting
approach.
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. Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data. In: PHM Society European Conference. Vol 5. ;
2020.'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2020). Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data. PHM Society European Conference,
5(1).
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM
Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender,
Amelie and Sextro, Walter}, year={2020} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation
of Time Series Forecasting Approaches for the Reliable Crack Length Prediction
of Riveted Aluminium Plates given Insufficient Data.” In PHM Society European
Conference, Vol. 5, 2020.
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data,” in PHM Society European Conference, 2020, vol.
5, no. 1.
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting
Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates
given Insufficient Data.” PHM Society European Conference, vol. 5, no.
1, 2020.
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference,
2020.'
date_created: 2020-08-11T13:32:40Z
date_updated: 2023-09-22T09:13:16Z
department:
- _id: '151'
intvolume: ' 5'
issue: '1'
keyword:
- PHM 2019
- crack propagation
- forecasting
- unevenly spaced time series
- step ahead prediction
- short time series
language:
- iso: eng
publication: PHM Society European Conference
quality_controlled: '1'
status: public
title: Evaluation of time series forecasting approaches for the reliable crack length
prediction of riveted aluminium plates given insufficient data
type: conference
user_id: '9557'
volume: 5
year: '2020'
...