---
_id: '25046'
abstract:
- lang: eng
  text: <jats:p>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.</jats:p>
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. <i>Machines</i>. 2021;9(10). doi:<a href="https://doi.org/10.3390/machines9100210">10.3390/machines9100210</a>
  apa: Bender, A. (2021). A Multi-Model-Particle Filtering-Based Prognostic Approach
    to Consider Uncertainties in RUL Predictions. <i>Machines</i>, <i>9</i>(10), Article
    210. <a href="https://doi.org/10.3390/machines9100210">https://doi.org/10.3390/machines9100210</a>
  bibtex: '@article{Bender_2021, title={A Multi-Model-Particle Filtering-Based Prognostic
    Approach to Consider Uncertainties in RUL Predictions}, volume={9}, DOI={<a href="https://doi.org/10.3390/machines9100210">10.3390/machines9100210</a>},
    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.” <i>Machines</i> 9, no. 10 (2021).
    <a href="https://doi.org/10.3390/machines9100210">https://doi.org/10.3390/machines9100210</a>.
  ieee: 'A. Bender, “A Multi-Model-Particle Filtering-Based Prognostic Approach to
    Consider Uncertainties in RUL Predictions,” <i>Machines</i>, vol. 9, no. 10, Art.
    no. 210, 2021, doi: <a href="https://doi.org/10.3390/machines9100210">10.3390/machines9100210</a>.'
  mla: Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach
    to Consider Uncertainties in RUL Predictions.” <i>Machines</i>, vol. 9, no. 10,
    210, 2021, doi:<a href="https://doi.org/10.3390/machines9100210">10.3390/machines9100210</a>.
  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'
...