A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions

A. Bender, Machines 9 (2021).

Journal Article | Published | English
Author
Abstract
<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>
Publishing Year
Journal Title
Machines
Volume
9
Issue
10
Article Number
210
ISSN
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Cite this

Bender A. A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions. Machines. 2021;9(10). doi:10.3390/machines9100210
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
@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} }
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.
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.
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.
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