---
_id: '55336'
abstract:
- lang: eng
text: "Predicting the remaining useful life of technical \r\nsystems has gained
significant attention in recent years due to \r\nincreasing demands for extending
the lifespan of degrading system \r\ncomponents. Therefore, already used systems
are retrofitted by \r\nintegrating sensors to monitor their performance and \r\nfunctionality,
enabling accurate diagnosis of their condition and \r\nprediction of their remaining
useful life. One of the main \r\nchallenges in this field is identified in the
missing data from the \r\ntime where the retrofitted system has already run but
without \r\nbeing monitored by sensors. In this paper, a novel approach for \r\nthe
combined diagnostics and prognostics of retrofitted systems is \r\nproposed. The
methodology aims to provide an accurate diagnosis \r\nof the system’s health state
and estimation of the remaining useful \r\nlife by a combination of a machine
learning and expert knowledge. \r\nTo evaluate the effectiveness of the proposed
methodology, a case \r\nstudy involving a retrofitted system in an industrial
setting is \r\nselected and applied. It is demonstrated that the approach \r\neffectively
diagnose the current system’s health state and \r\naccurately predict its remaining
useful life, thereby enabling \r\npredictive maintenance and decision-making.
Overall, our \r\nresearch contributes to advancing the field of condition \r\nmonitoring
for retrofitted systems by providing a comprehensive \r\nmethodology that addresses
the challenge of missing data."
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Bender A, Aimiyekagbon OK, Sextro W. Diagnostics and Prognostics for Retrofitted
Systems: A Comprehensive Approach for Enhanced System Health Assessment. In: Proceedings
of the 2024 Prognostics and System Health Management Conference (PHM). IEEE
Computer Society; 2024. doi:10.1109/PHM61473.2024.00038'
apa: 'Bender, A., Aimiyekagbon, O. K., & Sextro, W. (2024). Diagnostics and
Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System
Health Assessment. Proceedings of the 2024 Prognostics and System Health Management
Conference (PHM). 2024 Prognostics and System Health Management Conference
(PHM), Stockholm, Schweden. https://doi.org/10.1109/PHM61473.2024.00038'
bibtex: '@inproceedings{Bender_Aimiyekagbon_Sextro_2024, title={Diagnostics and
Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System
Health Assessment}, DOI={10.1109/PHM61473.2024.00038},
booktitle={Proceedings of the 2024 Prognostics and System Health Management Conference
(PHM)}, publisher={IEEE Computer Society}, author={Bender, Amelie and Aimiyekagbon,
Osarenren Kennedy and Sextro, Walter}, year={2024} }'
chicago: 'Bender, Amelie, Osarenren Kennedy Aimiyekagbon, and Walter Sextro. “Diagnostics
and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced
System Health Assessment.” In Proceedings of the 2024 Prognostics and System
Health Management Conference (PHM). IEEE Computer Society, 2024. https://doi.org/10.1109/PHM61473.2024.00038.'
ieee: 'A. Bender, O. K. Aimiyekagbon, and W. Sextro, “Diagnostics and Prognostics
for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment,”
presented at the 2024 Prognostics and System Health Management Conference (PHM),
Stockholm, Schweden, 2024, doi: 10.1109/PHM61473.2024.00038.'
mla: 'Bender, Amelie, et al. “Diagnostics and Prognostics for Retrofitted Systems:
A Comprehensive Approach for Enhanced System Health Assessment.” Proceedings
of the 2024 Prognostics and System Health Management Conference (PHM), IEEE
Computer Society, 2024, doi:10.1109/PHM61473.2024.00038.'
short: 'A. Bender, O.K. Aimiyekagbon, W. Sextro, in: Proceedings of the 2024 Prognostics
and System Health Management Conference (PHM), IEEE Computer Society, 2024.'
conference:
end_date: 2024-05-31
location: Stockholm, Schweden
name: 2024 Prognostics and System Health Management Conference (PHM)
start_date: 2024-05-28
date_created: 2024-07-22T09:27:57Z
date_updated: 2024-07-22T09:29:26Z
department:
- _id: '151'
doi: 10.1109/PHM61473.2024.00038
keyword:
- retrofit
- diagnosis
- prognostics
- RUL prediction
- missing data
- ball bearings
language:
- iso: eng
publication: Proceedings of the 2024 Prognostics and System Health Management Conference
(PHM)
publication_identifier:
isbn:
- 979-8-3503-6058-5
publisher: IEEE Computer Society
quality_controlled: '1'
status: public
title: 'Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach
for Enhanced System Health Assessment'
type: conference
user_id: '54290'
year: '2024'
...
---
_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: '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: '13460'
abstract:
- lang: eng
text: 'Remaining useful lifetime (RUL) predictions as part of a condition monitoring
system are focused in more and more research and industrial applications. To establish
an efficient and precise estimate of the RUL of a technical product, different uncertainties have to be handled. To minimize the uncertainties of the RUL estimation, a reliable
and accurate prognostic approach as well as a good failure threshold are important.
Regarding the failure threshold, most often an expert sets a fixed failure threshold. However, neither the a priori known failure threshold nor a fixedthreshold
value are feasible in every application. Especially in the case of varying characteristics
of the monitored system, an adaptive failure threshold is of great importance
concerning the accuracy of the RUL estimation. Rubber-metal-elements, which are
used in a wide range of applications for vibration and sound isolation, are mon-itored
by thermocouples to allow for lifetime predictions. Therefore, the element’s state
is described by its temper-ature during its service life. Aiming to establish
accurate RUL predictions of a rubber-metal-element, uncertainties due to nonlinear
material characteristics and changing operational conditions have to be considered.
Consequently, different temperature-based failure threshold definitions are implemented
and compared within a particle filtering approach. '
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Lennart
full_name: Schinke, Lennart
last_name: Schinke
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Bender A, Schinke L, Sextro W. Remaining useful lifetime prediction based
on adaptive failure thresholds. In: Beer M, Zio E, eds. Proceedings of the
29th European Safety and Reliability Conference (ESREL2019). ; 2019:1262-1269.'
apa: Bender, A., Schinke, L., & Sextro, W. (2019). Remaining useful lifetime
prediction based on adaptive failure thresholds. In M. Beer & E. Zio (Eds.),
Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)
(Issue 29, pp. 1262–1269).
bibtex: '@inproceedings{Bender_Schinke_Sextro_2019, title={Remaining useful lifetime
prediction based on adaptive failure thresholds}, number={29}, booktitle={Proceedings
of the 29th European Safety and Reliability Conference (ESREL2019)}, author={Bender,
Amelie and Schinke, Lennart and Sextro, Walter}, editor={Beer, Michael and Zio,
Enrico}, year={2019}, pages={1262–1269} }'
chicago: Bender, Amelie, Lennart Schinke, and Walter Sextro. “Remaining Useful Lifetime
Prediction Based on Adaptive Failure Thresholds.” In Proceedings of the 29th
European Safety and Reliability Conference (ESREL2019), edited by Michael
Beer and Enrico Zio, 1262–69, 2019.
ieee: A. Bender, L. Schinke, and W. Sextro, “Remaining useful lifetime prediction
based on adaptive failure thresholds,” in Proceedings of the 29th European
Safety and Reliability Conference (ESREL2019), Hannover, 2019, no. 29, pp.
1262–1269.
mla: Bender, Amelie, et al. “Remaining Useful Lifetime Prediction Based on Adaptive
Failure Thresholds.” Proceedings of the 29th European Safety and Reliability
Conference (ESREL2019), edited by Michael Beer and Enrico Zio, no. 29, 2019,
pp. 1262–69.
short: 'A. Bender, L. Schinke, W. Sextro, in: M. Beer, E. Zio (Eds.), Proceedings
of the 29th European Safety and Reliability Conference (ESREL2019), 2019, pp.
1262–1269.'
conference:
end_date: 2019.09.26
location: Hannover
name: '29th European Safety and Reliability Conference '
start_date: 2019.09.22
date_created: 2019-09-30T08:49:19Z
date_updated: 2023-09-22T07:31:53Z
department:
- _id: '151'
editor:
- first_name: Michael
full_name: Beer, Michael
last_name: Beer
- first_name: Enrico
full_name: Zio, Enrico
last_name: Zio
issue: '29'
keyword:
- RUL prediction
- adaptive threshold
- prognostics
- condition monitoring
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://rpsonline.com.sg/proceedings/9789811127243/html/0445.xml
oa: '1'
page: 1262-1269
publication: Proceedings of the 29th European Safety and Reliability Conference (ESREL2019)
publication_identifier:
isbn:
- 978-981-11-2724-3
publication_status: published
quality_controlled: '1'
status: public
title: Remaining useful lifetime prediction based on adaptive failure thresholds
type: conference
user_id: '54290'
year: '2019'
...
---
_id: '9947'
abstract:
- lang: eng
text: This paper presents a comparison of a number of prognostic methods with regard
to algorithm complexity and performance based on prognostic metrics. This information
serves as a guide for selection and design of prognostic systems for real-time
condition monitoring of technical systems. The methods are evaluated on ability
to estimate the remaining useful life of rolling element bearing. Run-to failure
vibration and temperature data is used in the analysis. The sampled prognostic
methods include wear-temperature correlation method, health state estimation using
temperature measurement, a multi-model particle filter approach with model parameter
adaptation utilizing temperature measurements, prognostics through health state
estimation and mapping extracted features to the remaining useful life through
regression approach. Although the performance of the methods utilizing the vibration
measurements is much better than the methods using temperature measurements, the
methods using temperature measurements are quite promising in terms of reducing
the overall cost of the condition monitoring system as well as the computational
time. An ensemble of the presented methods through weighted average is also introduced.
The results show that the methods are able to estimate the remaining useful life
within error bounds of +-15\%, which can be further reduced to +-5\% with the
ensemble approach.
author:
- first_name: James Kuria
full_name: Kimotho, James Kuria
last_name: Kimotho
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Sextro W. Comparison and ensemble of temperature-based and vibration-based
methods for machinery prognostics. In: Annual Conference of the Prognostics
and Health Management Society 2015. Vol 6. ; 2015.'
apa: Kimotho, J. K., & Sextro, W. (2015). Comparison and ensemble of temperature-based
and vibration-based methods for machinery prognostics. In Annual Conference
of the Prognostics and Health Management Society 2015 (Vol. 6).
bibtex: '@inproceedings{Kimotho_Sextro_2015, title={Comparison and ensemble of temperature-based
and vibration-based methods for machinery prognostics}, volume={6}, booktitle={Annual
Conference of the Prognostics and Health Management Society 2015}, author={Kimotho,
James Kuria and Sextro, Walter}, year={2015} }'
chicago: Kimotho, James Kuria, and Walter Sextro. “Comparison and Ensemble of Temperature-Based
and Vibration-Based Methods for Machinery Prognostics.” In Annual Conference
of the Prognostics and Health Management Society 2015, Vol. 6, 2015.
ieee: J. K. Kimotho and W. Sextro, “Comparison and ensemble of temperature-based
and vibration-based methods for machinery prognostics,” in Annual Conference
of the Prognostics and Health Management Society 2015, 2015, vol. 6.
mla: Kimotho, James Kuria, and Walter Sextro. “Comparison and Ensemble of Temperature-Based
and Vibration-Based Methods for Machinery Prognostics.” Annual Conference of
the Prognostics and Health Management Society 2015, vol. 6, 2015.
short: 'J.K. Kimotho, W. Sextro, in: Annual Conference of the Prognostics and Health
Management Society 2015, 2015.'
date_created: 2019-05-27T08:24:50Z
date_updated: 2019-05-27T08:25:44Z
department:
- _id: '151'
intvolume: ' 6'
keyword:
- ensemble methods
- combined prognostics
- data fusion
language:
- iso: eng
publication: Annual Conference of the Prognostics and Health Management Society 2015
status: public
title: Comparison and ensemble of temperature-based and vibration-based methods for
machinery prognostics
type: conference
user_id: '55222'
volume: 6
year: '2015'
...
---
_id: '9879'
abstract:
- lang: eng
text: Application of prognostics and health management (PHM) in the field of Proton
Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing
the reliability and availability of these systems. Though a lot of work is currently
being conducted to develop PHM systems for fuel cells, various challenges have
been encountered including the self-healing effect after characterization as well
as accelerated degradation due to dynamic loading, all which make RUL predictions
a difficult task. In this study, a prognostic approach based on adaptive particle
filter algorithm is proposed. The novelty of the proposed method lies in the introduction
of a self-healing factor after each characterization and the adaption of the degradation
model parameters to fit to the changing degradation trend. An ensemble of five
different state models based on weighted mean is then developed. The results show
that the method is effective in estimating the remaining useful life of PEM fuel
cells, with majority of the predictions falling within 5\% error. The method was
employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the
winner of the RUL category of the challenge.
author:
- first_name: 'James Kuria '
full_name: 'Kimotho, James Kuria '
last_name: Kimotho
- first_name: Tobias
full_name: Meyer, Tobias
last_name: Meyer
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter
with model parameter adaptation. In: Prognostics and Health Management (PHM),
2014 IEEE Conference On. ; 2014:1-6. doi:10.1109/ICPHM.2014.7036406'
apa: Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics
using particle filter with model parameter adaptation. In Prognostics and Health
Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406
bibtex: '@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics
using particle filter with model parameter adaptation}, DOI={10.1109/ICPHM.2014.7036406},
booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on},
author={Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}, year={2014},
pages={1–6} }'
chicago: Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell
Prognostics Using Particle Filter with Model Parameter Adaptation.” In Prognostics
and Health Management (PHM), 2014 IEEE Conference On, 1–6, 2014. https://doi.org/10.1109/ICPHM.2014.7036406.
ieee: J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle
filter with model parameter adaptation,” in Prognostics and Health Management
(PHM), 2014 IEEE Conference on, 2014, pp. 1–6.
mla: Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter
with Model Parameter Adaptation.” Prognostics and Health Management (PHM),
2014 IEEE Conference On, 2014, pp. 1–6, doi:10.1109/ICPHM.2014.7036406.
short: 'J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management
(PHM), 2014 IEEE Conference On, 2014, pp. 1–6.'
date_created: 2019-05-20T13:11:02Z
date_updated: 2019-05-20T13:12:27Z
department:
- _id: '151'
doi: 10.1109/ICPHM.2014.7036406
keyword:
- ageing
- particle filtering (numerical methods)
- proton exchange membrane fuel cells
- remaining life assessment
- PEM fuel cell prognostics
- PHM
- RUL predictions
- accelerated degradation
- adaptive particle filter algorithm
- dynamic loading
- model parameter adaptation
- prognostics and health management
- proton exchange membrane fuel cells
- remaining useful life estimation
- self-healing effect
- Adaptation models
- Data models
- Degradation
- Estimation
- Fuel cells
- Mathematical model
- Prognostics and health management
language:
- iso: eng
page: 1-6
publication: Prognostics and Health Management (PHM), 2014 IEEE Conference on
status: public
title: PEM fuel cell prognostics using particle filter with model parameter adaptation
type: conference
user_id: '55222'
year: '2014'
...