---
_id: '22724'
abstract:
- lang: eng
text: "\r\nPredictive Maintenance as a desirable maintenance strategy in industrial
applications relies on suitable condition monitoring solutions to reduce costs
and risks of the monitored technical systems. In general, those solutions utilize
model-based or data-driven methods to diagnose the current state or predict future
states of monitored technical systems. However, both methods have their advantages
and drawbacks. Combining both methods can improve uncertainty consideration and
accuracy. Different combination approaches of those hybrid methods exist to exploit
synergy effects. The choice of an appropriate approach depends on different requirements
and the goal behind the selection of a hybrid approach.\r\n\r\n \r\n\r\nIn this
work, the hybrid approach for estimating remaining useful lifetime takes potential
uncertainties into account. Therefore, a data-driven estimation of new measurements
is integrated within a model-based method. To consider uncertainties within the
system, a differentiation between different system behavior is realized throughout
diverse states of degradation.\r\n\r\nThe developed hybrid prediction approach
bases on a particle filtering method combined with a machine learning method,
to estimate the remaining useful lifetime of technical systems. Particle filtering
as a Monte Carlo simulation technique is suitable to map and propagate uncertainties.
Moreover, it is a state-of-the-art model-based method for predicting remaining
useful lifetime of technical systems. To integrate uncertainties a multi-model
particle filtering approach is employed. In general, resampling as a part of the
particle filtering approach has the potential to lead to an accurate prediction.
However, in the case where no future measurements are available, it may increase
the uncertainty of the prediction. By estimating new measurements, those uncertainties
are reduced within the data-driven part of the approach. Hence, both parts of
the hybrid approach strive to account for and reduce uncertainties.\r\n\r\n \r\n\r\nRubber-metal-elements
are employed as a use-case to evaluate the developed approach. Rubber-metal-elements,
which are used to isolate vibrations in various systems, such as railways, trucks
and wind turbines, show various uncertainties in their behavior and their degradation.
Those uncertainties are caused by diverse inner and outer factors, such as manufacturing
influences and operating conditions. By expert knowledge the influences are described,
analyzed and if possible reduced. However, the remaining uncertainties are considered
within the hybrid prediction method. Relative temperature is the selected measurand
to describe the element’s degradation. In lifetime tests, it is measured as the
difference between the element’s temperature and the ambient temperature. Thereby,
the influence of the ambient temperature on the element’s temperature is taken
into account. Those elements show three typical states of degradation that are
identified within the temperature measurements. Depending on the particular state
of degradation a new measurement is estimated within the hybrid approach to reduce
potential uncertainties.\r\n\r\nFinally, the performance of the developed hybrid
method is compared to a model-based method for estimating the remaining useful
lifetime of the same elements. Suitable performance indices are implemented to
underline the differences between the results."
author:
- 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: 'Bender A, Sextro W. Hybrid Prediction Method for Remaining Useful Lifetime
Estimation Considering Uncertainties. In: Do P, King S, Fink Olga, eds. Proceedings
of the European Conference of the PHM Society 2021. Vol 6. ; 2021. doi:https://doi.org/10.36001/phme.2021.v6i1.2843
'
apa: Bender, A., & Sextro, W. (2021). Hybrid Prediction Method for Remaining
Useful Lifetime Estimation Considering Uncertainties. In P. Do, S. King, & Olga
Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021
(Vol. 6, Issue 1). https://doi.org/10.36001/phme.2021.v6i1.2843
bibtex: '@inproceedings{Bender_Sextro_2021, title={Hybrid Prediction Method for
Remaining Useful Lifetime Estimation Considering Uncertainties}, volume={6}, DOI={https://doi.org/10.36001/phme.2021.v6i1.2843
}, number={1}, booktitle={Proceedings of the European Conference of the PHM
Society 2021}, author={Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and
King, Steve and Fink, Olga}, year={2021} }'
chicago: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining
Useful Lifetime Estimation Considering Uncertainties.” In Proceedings of the
European Conference of the PHM Society 2021, edited by Phuc Do, Steve King,
and Olga Fink, Vol. 6, 2021. https://doi.org/10.36001/phme.2021.v6i1.2843 .
ieee: 'A. Bender and W. Sextro, “Hybrid Prediction Method for Remaining Useful Lifetime
Estimation Considering Uncertainties,” in Proceedings of the European Conference
of the PHM Society 2021, 2021, vol. 6, no. 1, doi: https://doi.org/10.36001/phme.2021.v6i1.2843 .'
mla: Bender, Amelie, and Walter Sextro. “Hybrid Prediction Method for Remaining
Useful Lifetime Estimation Considering Uncertainties.” Proceedings of the European
Conference of the PHM Society 2021, edited by Phuc Do et al., vol. 6, no.
1, 2021, doi:https://doi.org/10.36001/phme.2021.v6i1.2843
.
short: 'A. Bender, W. Sextro, in: P. Do, S. King, Olga Fink (Eds.), Proceedings
of the European Conference of the PHM Society 2021, 2021.'
conference:
end_date: 2021-07-02
name: 6th European Conference of Prognostics and Health Management
start_date: 2021-06-28
date_created: 2021-07-14T06:29:08Z
date_updated: 2023-09-22T07:19:48Z
department:
- _id: '151'
doi: 'https://doi.org/10.36001/phme.2021.v6i1.2843 '
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:
- Hybrid prediction method
- Multi-model particle filtering
- Uncertainty quantification
- RUL estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.phmsociety.org/index.php/phme/article/view/2843
oa: '1'
publication: Proceedings of the European Conference of the PHM Society 2021
publication_identifier:
unknown:
- 978-1-936263-34-9
publication_status: published
quality_controlled: '1'
status: public
title: Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering
Uncertainties
type: conference
user_id: '54290'
volume: 6
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'
...
---
_id: '5663'
abstract:
- lang: eng
text: Spam as unsolicited e-mail to a large number of recipients is known to ecome
an increasingly disturbing and costly issue of electronic business and internet
traffic. Mainly technical-oriented approaches are applied with a focus on blocking,
filtering, and authentication mechanisms based on the domain name system. They
come along with different drawbacks and have all low effectiveness in common.
The article sketches these approaches, shows its limitations, and proposes an
account-based approach where the number of e-mails per day and account is restricted.
author:
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
citation:
ama: 'Schryen G. Fighting Spam: Motivating an Account-based Approach. In: Isaias
P, ed. Proceedings of the IADIS International Conference WWW/Internet 2004.
Vol. 2. Madrid: IADIS Press; 2004:937-940.'
apa: 'Schryen, G. (2004). Fighting Spam: Motivating an Account-based Approach. In
P. Isaias (Ed.), Proceedings of the IADIS International Conference WWW/Internet
2004. vol. 2 (pp. 937–940). Madrid: IADIS Press.'
bibtex: '@inproceedings{Schryen_2004, place={Madrid}, title={Fighting Spam: Motivating
an Account-based Approach}, booktitle={Proceedings of the IADIS International
Conference WWW/Internet 2004. vol. 2}, publisher={IADIS Press}, author={Schryen,
Guido}, editor={Isaias, PedroEditor}, year={2004}, pages={937–940} }'
chicago: 'Schryen, Guido. “Fighting Spam: Motivating an Account-Based Approach.”
In Proceedings of the IADIS International Conference WWW/Internet 2004. Vol.
2, edited by Pedro Isaias, 937–40. Madrid: IADIS Press, 2004.'
ieee: 'G. Schryen, “Fighting Spam: Motivating an Account-based Approach,” in Proceedings
of the IADIS International Conference WWW/Internet 2004. vol. 2, 2004, pp.
937–940.'
mla: 'Schryen, Guido. “Fighting Spam: Motivating an Account-Based Approach.” Proceedings
of the IADIS International Conference WWW/Internet 2004. Vol. 2, edited by
Pedro Isaias, IADIS Press, 2004, pp. 937–40.'
short: 'G. Schryen, in: P. Isaias (Ed.), Proceedings of the IADIS International
Conference WWW/Internet 2004. Vol. 2, IADIS Press, Madrid, 2004, pp. 937–940.'
date_created: 2018-11-14T14:59:03Z
date_updated: 2022-01-06T07:02:25Z
ddc:
- '000'
department:
- _id: '277'
editor:
- first_name: Pedro
full_name: Isaias, Pedro
last_name: Isaias
extern: '1'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2018-12-18T13:33:30Z
date_updated: 2018-12-18T13:33:30Z
file_id: '6333'
file_name: Fighting Spam_Motivating an account-based approach.pdf
file_size: 46083
relation: main_file
file_date_updated: 2018-12-18T13:33:30Z
has_accepted_license: '1'
keyword:
- e-mail
- spam
- filtering
- blocking
- LMAP
- SMTP account
language:
- iso: eng
oa: '1'
page: 937-940
place: Madrid
publication: Proceedings of the IADIS International Conference WWW/Internet 2004.
vol. 2
publisher: IADIS Press
status: public
title: 'Fighting Spam: Motivating an Account-based Approach'
type: conference
user_id: '61579'
year: '2004'
...
---
_id: '11931'
abstract:
- lang: eng
text: The paper is concerned with binaural signal processing for a bimodal human-robot
interface with hearing and vision. The two microphone signals are processed to
obtain an enhanced single-channel input signal for the subsequent speech recognizer
and to localize the acoustic source, an important information for establishing
a natural human-robot communication. We utilize a robust adaptive algorithm for
filter-and-sum beamforming (FSB) and extract speaker direction information from
the resulting FIR filter coefficients. Further, particle filtering is applied
which conducts a nonlinear Bayesian tracking of speaker movement. Good location
accuracy can be achieved even in highly reverberant environments. The results
obtained outperform the conventional generalized cross correlation (GCC) method.
author:
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Warsitz E, Haeb-Umbach R. Robust speaker direction estimation with particle
filtering. In: IEEE Workshop on Multimedia Signal Processing (MMSP 2004).
; 2004:367-370. doi:10.1109/MMSP.2004.1436569'
apa: Warsitz, E., & Haeb-Umbach, R. (2004). Robust speaker direction estimation
with particle filtering. In IEEE Workshop on Multimedia Signal Processing (MMSP
2004) (pp. 367–370). https://doi.org/10.1109/MMSP.2004.1436569
bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2004, title={Robust speaker direction
estimation with particle filtering}, DOI={10.1109/MMSP.2004.1436569},
booktitle={IEEE Workshop on Multimedia Signal Processing (MMSP 2004)}, author={Warsitz,
Ernst and Haeb-Umbach, Reinhold}, year={2004}, pages={367–370} }'
chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” In IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 367–70, 2004. https://doi.org/10.1109/MMSP.2004.1436569.
ieee: E. Warsitz and R. Haeb-Umbach, “Robust speaker direction estimation with particle
filtering,” in IEEE Workshop on Multimedia Signal Processing (MMSP 2004),
2004, pp. 367–370.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” IEEE Workshop on Multimedia Signal Processing (MMSP
2004), 2004, pp. 367–70, doi:10.1109/MMSP.2004.1436569.
short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 2004, pp. 367–370.'
date_created: 2019-07-12T05:31:01Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/MMSP.2004.1436569
keyword:
- bimodal human-robot interface
- binaural signal processing
- enhanced single-channel input signal
- filter-and-sum beamforming
- filtering theory
- FIR filter coefficient
- generalized cross correlation method
- microphones
- microphone signal
- nonlinear Bayesian tracking
- particle filtering
- robust adaptive algorithm
- robust speaker direction estimation
- signal processing
- speech enhancement
- speech recognition
- speech recognizer
- user interfaces
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2004/WaHa04.pdf
oa: '1'
page: 367-370
publication: IEEE Workshop on Multimedia Signal Processing (MMSP 2004)
status: public
title: Robust speaker direction estimation with particle filtering
type: conference
user_id: '44006'
year: '2004'
...