---
_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: '9889'
abstract:
- lang: eng
text: A measurement method is presented that combines the advantages of the multisine
measurement technique with a prediction method for peak bending behavior. This
combination allows the analysis of the dynamic behavior of mechanical structures
at distinctly reduced measurement durations and has the advantage of reducing
high excitation impacts on the structure under test.
author:
- first_name: Christian
full_name: Sprock, Christian
last_name: Sprock
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Sprock C, Sextro W. Time-efficient dynamic analysis of structures exhibiting
nonlinear peak bending. In: Instrumentation and Measurement Technology Conference
(I2MTC) Proceedings, 2014 IEEE International. ; 2014:320-324. doi:10.1109/I2MTC.2014.6860760'
apa: Sprock, C., & Sextro, W. (2014). Time-efficient dynamic analysis of structures
exhibiting nonlinear peak bending. In Instrumentation and Measurement Technology
Conference (I2MTC) Proceedings, 2014 IEEE International (pp. 320–324). https://doi.org/10.1109/I2MTC.2014.6860760
bibtex: '@inproceedings{Sprock_Sextro_2014, title={Time-efficient dynamic analysis
of structures exhibiting nonlinear peak bending}, DOI={10.1109/I2MTC.2014.6860760},
booktitle={Instrumentation and Measurement Technology Conference (I2MTC) Proceedings,
2014 IEEE International}, author={Sprock, Christian and Sextro, Walter}, year={2014},
pages={320–324} }'
chicago: Sprock, Christian, and Walter Sextro. “Time-Efficient Dynamic Analysis
of Structures Exhibiting Nonlinear Peak Bending.” In Instrumentation and Measurement
Technology Conference (I2MTC) Proceedings, 2014 IEEE International, 320–24,
2014. https://doi.org/10.1109/I2MTC.2014.6860760.
ieee: C. Sprock and W. Sextro, “Time-efficient dynamic analysis of structures exhibiting
nonlinear peak bending,” in Instrumentation and Measurement Technology Conference
(I2MTC) Proceedings, 2014 IEEE International, 2014, pp. 320–324.
mla: Sprock, Christian, and Walter Sextro. “Time-Efficient Dynamic Analysis of Structures
Exhibiting Nonlinear Peak Bending.” Instrumentation and Measurement Technology
Conference (I2MTC) Proceedings, 2014 IEEE International, 2014, pp. 320–24,
doi:10.1109/I2MTC.2014.6860760.
short: 'C. Sprock, W. Sextro, in: Instrumentation and Measurement Technology Conference
(I2MTC) Proceedings, 2014 IEEE International, 2014, pp. 320–324.'
date_created: 2019-05-20T13:25:22Z
date_updated: 2019-05-20T13:25:53Z
department:
- _id: '151'
doi: 10.1109/I2MTC.2014.6860760
keyword:
- bending
- dynamic testing
- measurement
- structural engineering
- vibrations
- measurement durations
- mechanical structures
- multisine measurement technique
- nonlinear peak bending behavior
- prediction method
- time-efficient dynamic analysis
- Heuristic algorithms
- Nonlinear systems
- Oscillators
- Time measurement
- Time-frequency analysis
- Vibrations
language:
- iso: eng
page: 320-324
publication: Instrumentation and Measurement Technology Conference (I2MTC) Proceedings,
2014 IEEE International
status: public
title: Time-efficient dynamic analysis of structures exhibiting nonlinear peak bending
type: conference
user_id: '55222'
year: '2014'
...