---
_id: '44672'
abstract:
- lang: eng
text: With enhancing digitalization, condition monitoring is used in an increasing
number of application fields across various industrial sectors. By its application,
increased reliability as well as reduced risks and costs can be achieved. Based
on different approaches, technical systems are monitored and measured data is
analyzed to enable condition-based or predictive maintenance. To this end, machine
learning approaches are usually implemented to diagnose the health states or predict
the health index of the monitored system. However, these trained models are often
black-box models, not intuitively explainable for a human. To overcome this shortcoming,
a model-based approach based on physics is developed for piezoelectric bending
actuators. Such a model enables a transparent representation of the system. Moreover,
the model-based approach is extended by a parameter-estimation to account for
sudden changes in behavior e. g. caused by occurring cracks.
article_number: '114399'
article_type: original
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
citation:
ama: 'Bender A. Model-based condition monitoring of piezoelectric bending actuators.
Sensors and Actuators A: Physical. 2023;357. doi:10.1016/j.sna.2023.114399'
apa: 'Bender, A. (2023). Model-based condition monitoring of piezoelectric bending
actuators. Sensors and Actuators A: Physical, 357, Article 114399.
https://doi.org/10.1016/j.sna.2023.114399'
bibtex: '@article{Bender_2023, title={Model-based condition monitoring of piezoelectric
bending actuators}, volume={357}, DOI={10.1016/j.sna.2023.114399},
number={114399}, journal={Sensors and Actuators A: Physical}, publisher={Elsevier
BV}, author={Bender, Amelie}, year={2023} }'
chicago: 'Bender, Amelie. “Model-Based Condition Monitoring of Piezoelectric Bending
Actuators.” Sensors and Actuators A: Physical 357 (2023). https://doi.org/10.1016/j.sna.2023.114399.'
ieee: 'A. Bender, “Model-based condition monitoring of piezoelectric bending actuators,”
Sensors and Actuators A: Physical, vol. 357, Art. no. 114399, 2023, doi:
10.1016/j.sna.2023.114399.'
mla: 'Bender, Amelie. “Model-Based Condition Monitoring of Piezoelectric Bending
Actuators.” Sensors and Actuators A: Physical, vol. 357, 114399, Elsevier
BV, 2023, doi:10.1016/j.sna.2023.114399.'
short: 'A. Bender, Sensors and Actuators A: Physical 357 (2023).'
date_created: 2023-05-09T09:49:44Z
date_updated: 2023-05-09T09:53:31Z
department:
- _id: '151'
doi: 10.1016/j.sna.2023.114399
intvolume: ' 357'
keyword:
- Condition Monitoring
- Model-based approach Diagnostics
- Varying conditions
- Explainability
- Piezoelectric bending actuators
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://authors.elsevier.com/a/1h2WV3IC9dF7Hm
oa: '1'
publication: 'Sensors and Actuators A: Physical'
publication_identifier:
issn:
- 0924-4247
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: Model-based condition monitoring of piezoelectric bending actuators
type: journal_article
user_id: '54290'
volume: 357
year: '2023'
...
---
_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'
...