---
_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: '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'
...
---
_id: '11943'
abstract:
- lang: eng
text: A marginalized particle filter is proposed for performing single channel speech
enhancement with a non-linear dynamic state model. The system consists of a particle
filter for tracking line spectral pair (LSP) parameters and a Kalman filter per
particle for speech enhancement. The state model for the LSPs has been learnt
on clean speech training data. In our approach parameters and speech samples are
processed at different time scales by assuming the parameters to be constant for
small blocks of data. Further enhancement is obtained by an iteration which can
be applied on these small blocks. The experiments show that similar SNR gains
are obtained as with the Kalman-LM-iterative algorithm. However better values
of the noise level and the log-spectral distance are achieved
author:
- first_name: Stefan
full_name: Windmann, Stefan
last_name: Windmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear
Dynamic State Model of Speech and its Parameters. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006). Vol 1. ; 2006:I.
doi:10.1109/ICASSP.2006.1660058'
apa: Windmann, S., & Haeb-Umbach, R. (2006). Iterative Speech Enhancement using
a Non-Linear Dynamic State Model of Speech and its Parameters. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) (Vol.
1, p. I). https://doi.org/10.1109/ICASSP.2006.1660058
bibtex: '@inproceedings{Windmann_Haeb-Umbach_2006, title={Iterative Speech Enhancement
using a Non-Linear Dynamic State Model of Speech and its Parameters}, volume={1},
DOI={10.1109/ICASSP.2006.1660058},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006},
pages={I} }'
chicago: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement
Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006),
1:I, 2006. https://doi.org/10.1109/ICASSP.2006.1660058.
ieee: S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear
Dynamic State Model of Speech and its Parameters,” in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, vol. 1, p.
I.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using
a Non-Linear Dynamic State Model of Speech and Its Parameters.” IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), vol.
1, 2006, p. I, doi:10.1109/ICASSP.2006.1660058.
short: 'S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2006), 2006, p. I.'
date_created: 2019-07-12T05:31:15Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2006.1660058
intvolume: ' 1'
keyword:
- clean speech training data
- iterative methods
- iterative speech enhancement
- Kalman filter
- Kalman filters
- Kalman-LM-iterative algorithm
- line spectral pair parameters
- log-spectral distance
- marginalized particle filter
- noise level
- nonlinear dynamic state speech model
- particle filtering (numerical methods)
- single channel speech enhancement
- SNR gains
- speech enhancement
- speech samples
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf
oa: '1'
page: I
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)
status: public
title: Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech
and its Parameters
type: conference
user_id: '44006'
volume: 1
year: '2006'
...