TU Darmstadt / ULB / TUprints

Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks

Götz, Benedict ; Kersting, Sebastian (2022)
Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks.
In: Applied Mechanics and Materials, 2018, 885
doi: 10.26083/tuprints-00020433
Article, Secondary publication, Publisher's Version

[img] Text
AMM.885.293.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (478kB)
Item Type: Article
Type of entry: Secondary publication
Title: Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2018
Publisher: Trans Tech Publications Ltd.
Journal or Publication Title: Applied Mechanics and Materials
Volume of the journal: 885
DOI: 10.26083/tuprints-00020433
Corresponding Links:
Origin: Secondary publication service
Abstract:

Quantification of uncertainty in technical systems is often based on surrogate models of corresponding simulation models. Usually, the underlying simulation model does not describe the reality perfectly, and consequently the surrogate model will be imperfect.In this article we propose an improved surrogate model of the vibration attenuation of a beam with shunted piezoelectric transducers. Therefore, experimentally observed and simulated variations in the vibration attenuation are combined in the model estimation process, by using multi-layer feedforward neural networks. Based on this improved surrogate model, we construct a density estimate of the maximal amplitude in the vibration attenuation.The density estimate is used to analyze the uncertainty in the vibration attenuation, resulting from manufacturing variations.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-204333
Additional Information:

Keywords: Density Estimation, Imperfect Model, Neural Network, Surrogate Model, Uncertainty Quantification

Classification DDC: 500 Science and mathematics > 510 Mathematics
600 Technology, medicine, applied sciences > 600 Technology
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Research group System Reliability, Adaptive Structures, and Machine Acoustics (SAM)
04 Department of Mathematics > Stochastik
Date Deposited: 02 Feb 2022 14:01
Last Modified: 22 Mar 2023 14:21
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20433
PPN: 50618871X
Export:
Actions (login required)
View Item View Item